Repository: sktime/sktime
Pull request #7524 (2024-12-14 10:01:15)

[DOC] document new `visual_block_kind` tag
This PR adds documentation for the new `visual_block_kind` tag introduced in https://github.com/sktime/sktime/pull/7233 by @mateuszkasprowicz, in the API reference for developers.

Probably by accident, the tag was added to the list of tags scheduled for deprecation in the tag registry, from where it has also been removed.


Patch:
diff --git a/.all-contributorsrc b/.all-contributorsrc
index f9ec09cba..1d0b821da 100644
--- a/.all-contributorsrc
+++ b/.all-contributorsrc
@@ -181,6 +181,15 @@
         "test"
       ]
     },
+    {
+      "login": "jgyasu",
+      "name": "Jigyasu",
+      "avatar_url": "https://avatars.githubusercontent.com/u/94278611?s=400&u=19941ae4723ef841b3a3c7df996214efadc98c45&v=4",
+      "profile": "https://www.linkedin.com/in/jgyasu/",
+      "contributions": [
+        "code"
+      ]
+    },
     {
       "login": "jesellier",
       "name": "jesellier",
@@ -3251,10 +3260,19 @@
       ]
     },
     {
-      "login":"Adarsh2345",
-      "name":"Adarsh J",
-      "avatar_url":"https://avatars.githubusercontent.com/u/130629672?v=4",
-      "profile":"https://github.com/Adarsh2345",
+      "login": "y-mx",
+      "name": "Yimeng Xie",
+      "avatar_url": "https://avatars.githubusercontent.com/y-mx",
+      "profile": "https://github.com/y-mx",
+      "contributions": [
+        "code"
+      ]
+    },
+    {
+      "login": "Adarsh2345",
+      "name": "Adarsh J",
+      "avatar_url": "https://avatars.githubusercontent.com/u/130629672?v=4",
+      "profile": "https://github.com/Adarsh2345",
       "contributions": [
          "doc"
       ]
@@ -3277,6 +3295,24 @@
         "doc",
         "code"
       ]
+    },
+    {
+      "login": "b9junkers",
+      "name": "Shounak Shirodkar",
+      "avatar_url": "https://avatars.githubusercontent.com/u/54496663?v=4",
+      "profile": "https://github.com/b9junkers",
+      "contributions": [
+        "doc"
+      ]
+    },
+    {
+      "login": "VjayRam",
+      "name": "Vijay Ram Enaganti",
+      "avatar_url": "https://avatars.githubusercontent.com/u/76694566?v=4",
+      "profile": "https://github.com/VjayRam",
+      "contributions": [
+        "doc"
+      ]
     }
   ]
 }
diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md
index 5d0c934c5..69fedd936 100644
--- a/CONTRIBUTORS.md
+++ b/CONTRIBUTORS.md
@@ -6,7 +6,7 @@ Contributors
 <!-- Please add your badges to .all-contibutorsrc -->
 
 <!-- ALL-CONTRIBUTORS-BADGE:START - Do not remove or modify this section -->
-[![All Contributors](https://img.shields.io/badge/all_contributors-322-orange.svg)](#contributors)
+[![All Contributors](https://img.shields.io/badge/all_contributors-324-orange.svg)](#contributors)
 <!-- ALL-CONTRIBUTORS-BADGE:END -->
 
 This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome!
@@ -174,6 +174,7 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
     </tr>
     <tr>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/javiber"><img src="https://avatars.githubusercontent.com/u/3588715?v=4?s=100" width="100px;" alt="Javier Berneche"/><br /><sub><b>Javier Berneche</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=javiber" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=javiber" title="Documentation">📖</a></td>
+      <td align="center" valign="top" width="11.11%"><a href="https://www.linkedin.com/in/jgyasu/"><img src="https://avatars.githubusercontent.com/u/94278611?s=400&u=19941ae4723ef841b3a3c7df996214efadc98c45&v=4?s=100" width="100px;" alt="Jigyasu"/><br /><sub><b>Jigyasu</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=jgyasu" title="Code">💻</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/Z-Fran"><img src="https://avatars.githubusercontent.com/u/49083766?v=4?s=100" width="100px;" alt="Jindong Zhang"/><br /><sub><b>Jindong Zhang</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=Z-Fran" title="Code">💻</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/tajir0"><img src="https://avatars.githubusercontent.com/u/36022362?s=100" width="100px;" alt="Jinrong Tang"/><br /><sub><b>Jinrong Tang</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=tajir0" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=tajir0" title="Tests">⚠️</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/pirnerjonas"><img src="https://avatars.githubusercontent.com/u/48887249?v=4?s=100" width="100px;" alt="Jonas Pirner"/><br /><sub><b>Jonas Pirner</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=pirnerjonas" title="Documentation">📖</a></td>
@@ -181,9 +182,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/jorenham"><img src="?s=100" width="100px;" alt="Joren Hammudoglu"/><br /><sub><b>Joren Hammudoglu</b></sub></a><br /><a href="#infra-jorenham" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://juanitorduz.github.io/"><img src="https://avatars1.githubusercontent.com/u/22996444?v=4?s=100" width="100px;" alt="Juan Orduz"/><br /><sub><b>Juan Orduz</b></sub></a><br /><a href="#tutorial-juanitorduz" title="Tutorials">✅</a> <a href="https://github.com/sktime/sktime/commits?author=juanitorduz" title="Documentation">📖</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/julia-kraus"><img src="https://avatars.githubusercontent.com/julia-kraus?s=100" width="100px;" alt="Julia Kraus"/><br /><sub><b>Julia Kraus</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=julia-kraus" title="Documentation">📖</a> <a href="https://github.com/sktime/sktime/commits?author=julia-kraus" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=julia-kraus" title="Tests">⚠️</a></td>
-      <td align="center" valign="top" width="11.11%"><a href="https://github.com/jelc53"><img src="?s=100" width="100px;" alt="Julian Cooper"/><br /><sub><b>Julian Cooper</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=jelc53" title="Code">💻</a> <a href="#ideas-jelc53" title="Ideas, Planning, & Feedback">🤔</a></td>
     </tr>
     <tr>
+      <td align="center" valign="top" width="11.11%"><a href="https://github.com/jelc53"><img src="?s=100" width="100px;" alt="Julian Cooper"/><br /><sub><b>Julian Cooper</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=jelc53" title="Code">💻</a> <a href="#ideas-jelc53" title="Ideas, Planning, & Feedback">🤔</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/helloplayer1"><img src="https://avatars.githubusercontent.com/u/32032467?v=4?s=100" width="100px;" alt="Julian Haderlein"/><br /><sub><b>Julian Haderlein</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=helloplayer1" title="Documentation">📖</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/julnow"><img src="https://avatars.githubusercontent.com/u/21206185?v=4?s=100" width="100px;" alt="Julian Nowak"/><br /><sub><b>Julian Nowak</b></sub></a><br /><a href="https://github.com/sktime/sktime/issues?q=author%3Ajulnow" title="Bug reports">🐛</a> <a href="https://github.com/sktime/sktime/commits?author=julnow" title="Code">💻</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://www.linkedin.com/in/julianarn/"><img src="https://avatars.githubusercontent.com/u/19613567?v=4?s=100" width="100px;" alt="Juliana"/><br /><sub><b>Juliana</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=julramos" title="Code">💻</a></td>
@@ -192,9 +193,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/kcc-lion"><img src="?s=100" width="100px;" alt="Kai Lion"/><br /><sub><b>Kai Lion</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=kcc-lion" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=kcc-lion" title="Tests">⚠️</a> <a href="https://github.com/sktime/sktime/commits?author=kcc-lion" title="Documentation">📖</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/kanand77"><img src="https://avatars.githubusercontent.com/kanand77?s=100" width="100px;" alt="Kavin Anand"/><br /><sub><b>Kavin Anand</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=kanand77" title="Documentation">📖</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://kejsitake.com/"><img src="https://avatars.githubusercontent.com/u/23707808?v=4?s=100" width="100px;" alt="Kejsi Take"/><br /><sub><b>Kejsi Take</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=kejsitake" title="Code">💻</a></td>
-      <td align="center" valign="top" width="11.11%"><a href="https://www.linkedin.com/in/kevinlam2"><img src="https://avatars.githubusercontent.com/u/114420932?s=400&v=4?s=100" width="100px;" alt="Kevin Lam"/><br /><sub><b>Kevin Lam</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=klam-data" title="Code">💻</a> <a href="#example-klam-data" title="Examples">💡</a> <a href="https://github.com/sktime/sktime/commits?author=klam-data" title="Tests">⚠️</a></td>
     </tr>
     <tr>
+      <td align="center" valign="top" width="11.11%"><a href="https://www.linkedin.com/in/kevinlam2"><img src="https://avatars.githubusercontent.com/u/114420932?s=400&v=4?s=100" width="100px;" alt="Kevin Lam"/><br /><sub><b>Kevin Lam</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=klam-data" title="Code">💻</a> <a href="#example-klam-data" title="Examples">💡</a> <a href="https://github.com/sktime/sktime/commits?author=klam-data" title="Tests">⚠️</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/kirilral"><img src="https://avatars.githubusercontent.com/u/34281484?v=4?s=100" width="100px;" alt="Kiril Ralinovski"/><br /><sub><b>Kiril Ralinovski</b></sub></a><br /><a href="#ideas-kirilral" title="Ideas, Planning, & Feedback">🤔</a> <a href="#mentoring-kirilral" title="Mentoring">🧑‍🏫</a> <a href="#projectManagement-kirilral" title="Project Management">📆</a> <a href="#talk-kirilral" title="Talks">📢</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://whitakerlab.github.io/"><img src="https://avatars1.githubusercontent.com/u/3626306?v=4?s=100" width="100px;" alt="Kirstie Whitaker"/><br /><sub><b>Kirstie Whitaker</b></sub></a><br /><a href="#ideas-KirstieJane" title="Ideas, Planning, & Feedback">🤔</a> <a href="#fundingFinding-KirstieJane" title="Funding Finding">🔍</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/kishmanani"><img src="https://avatars.githubusercontent.com/u/30973056?v=4?s=100" width="100px;" alt="Kishan Manani"/><br /><sub><b>Kishan Manani</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=KishManani" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=KishManani" title="Documentation">📖</a> <a href="https://github.com/sktime/sktime/commits?author=KishManani" title="Tests">⚠️</a> <a href="https://github.com/sktime/sktime/issues?q=author%3AKishManani" title="Bug reports">🐛</a> <a href="#ideas-KishManani" title="Ideas, Planning, & Feedback">🤔</a></td>
@@ -203,9 +204,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/koralturkk"><img src="https://avatars2.githubusercontent.com/u/18037789?s=460&v=4?s=100" width="100px;" alt="Kutay Koralturk"/><br /><sub><b>Kutay Koralturk</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=koralturkk" title="Code">💻</a> <a href="https://github.com/sktime/sktime/issues?q=author%3Akoralturkk" title="Bug reports">🐛</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/ltsaprounis"><img src="https://avatars.githubusercontent.com/u/64217214?v=4?s=100" width="100px;" alt="Leonidas Tsaprounis"/><br /><sub><b>Leonidas Tsaprounis</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=ltsaprounis" title="Code">💻</a> <a href="https://github.com/sktime/sktime/issues?q=author%3Altsaprounis" title="Bug reports">🐛</a> <a href="#mentoring-ltsaprounis" title="Mentoring">🧑‍🏫</a> <a href="https://github.com/sktime/sktime/pulls?q=is%3Apr+reviewed-by%3Altsaprounis" title="Reviewed Pull Requests">👀</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/lielleravid"><img src="https://avatars.githubusercontent.com/u/37774194?v=4?s=100" width="100px;" alt="Lielle Ravid"/><br /><sub><b>Lielle Ravid</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=lielleravid" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=lielleravid" title="Documentation">📖</a></td>
-      <td align="center" valign="top" width="11.11%"><a href="https://github.com/chillerobscuro"><img src="https://avatars.githubusercontent.com/u/5232872?v=4?s=100" width="100px;" alt="Logan Duffy"/><br /><sub><b>Logan Duffy</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=chillerobscuro" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=chillerobscuro" title="Documentation">📖</a> <a href="https://github.com/sktime/sktime/commits?author=chillerobscuro" title="Tests">⚠️</a> <a href="https://github.com/sktime/sktime/issues?q=author%3Achillerobscuro" title="Bug reports">🐛</a> <a href="#ideas-chillerobscuro" title="Ideas, Planning, & Feedback">🤔</a></td>
     </tr>
     <tr>
+      <td align="center" valign="top" width="11.11%"><a href="https://github.com/chillerobscuro"><img src="https://avatars.githubusercontent.com/u/5232872?v=4?s=100" width="100px;" alt="Logan Duffy"/><br /><sub><b>Logan Duffy</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=chillerobscuro" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=chillerobscuro" title="Documentation">📖</a> <a href="https://github.com/sktime/sktime/commits?author=chillerobscuro" title="Tests">⚠️</a> <a href="https://github.com/sktime/sktime/issues?q=author%3Achillerobscuro" title="Bug reports">🐛</a> <a href="#ideas-chillerobscuro" title="Ideas, Planning, & Feedback">🤔</a></td>
       <td align="center" valign="top" width="11.11%"><a href="http://lpantano.github.io/"><img src="https://avatars2.githubusercontent.com/u/1621788?v=4?s=100" width="100px;" alt="Lorena Pantano"/><br /><sub><b>Lorena Pantano</b></sub></a><br /><a href="#ideas-lpantano" title="Ideas, Planning, & Feedback">🤔</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/ltoniazzi"><img src="https://avatars.githubusercontent.com/u/61414566?s=100" width="100px;" alt="Lorenzo Toniazzi"/><br /><sub><b>Lorenzo Toniazzi</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=ltoniazzi" title="Code">💻</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/Lovkush-A"><img src="https://avatars.githubusercontent.com/u/25344832?v=4?s=100" width="100px;" alt="Lovkush"/><br /><sub><b>Lovkush</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=Lovkush-A" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=Lovkush-A" title="Tests">⚠️</a> <a href="#ideas-Lovkush-A" title="Ideas, Planning, & Feedback">🤔</a> <a href="#mentoring-Lovkush-A" title="Mentoring">🧑‍🏫</a> <a href="#projectManagement-Lovkush-A" title="Project Management">📆</a></td>
@@ -214,9 +215,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/luiszugasti"><img src="https://avatars.githubusercontent.com/u/11198457?s=460&u=0645b72683e491824aca16db9702f1d3eb990389&v=4?s=100" width="100px;" alt="Luis Zugasti"/><br /><sub><b>Luis Zugasti</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=luiszugasti" title="Documentation">📖</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/lmmentel"><img src="https://avatars.githubusercontent.com/u/8989838?v=4?s=100" width="100px;" alt="Lukasz Mentel"/><br /><sub><b>Lukasz Mentel</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=lmmentel" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=lmmentel" title="Documentation">📖</a> <a href="#infra-lmmentel" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a> <a href="https://github.com/sktime/sktime/commits?author=lmmentel" title="Tests">⚠️</a> <a href="https://github.com/sktime/sktime/issues?q=author%3Almmentel" title="Bug reports">🐛</a> <a href="#maintenance-lmmentel" title="Maintenance">🚧</a> <a href="#mentoring-lmmentel" title="Mentoring">🧑‍🏫</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://www.linkedin.com/in/madhuri-agarwal-166080209/"><img src="https://avatars.githubusercontent.com/u/108001973?v=4&size=64?s=100" width="100px;" alt="Madhuri"/><br /><sub><b>Madhuri</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=madhuri723" title="Documentation">📖</a></td>
-      <td align="center" valign="top" width="11.11%"><a href="https://github.com/manuel-munoz-aguirre"><img src="https://avatars.githubusercontent.com/u/5576458?v=4?s=100" width="100px;" alt="Manuel Muñoz Aguirre"/><br /><sub><b>Manuel Muñoz Aguirre</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=manuel-munoz-aguirre" title="Documentation">📖</a></td>
     </tr>
     <tr>
+      <td align="center" valign="top" width="11.11%"><a href="https://github.com/manuel-munoz-aguirre"><img src="https://avatars.githubusercontent.com/u/5576458?v=4?s=100" width="100px;" alt="Manuel Muñoz Aguirre"/><br /><sub><b>Manuel Muñoz Aguirre</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=manuel-munoz-aguirre" title="Documentation">📖</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/manolotis"><img src="https://avatars.githubusercontent.com/manolotis?s=100" width="100px;" alt="Manuel Muñoz Sánchez"/><br /><sub><b>Manuel Muñoz Sánchez</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=manolotis" title="Documentation">📖</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/marrov"><img src="https://avatars.githubusercontent.com/u/54272586?v=4?s=100" width="100px;" alt="Marc Rovira"/><br /><sub><b>Marc Rovira</b></sub></a><br /><a href="#design-marrov" title="Design">🎨</a> <a href="https://github.com/sktime/sktime/commits?author=marrov" title="Documentation">📖</a> <a href="#ideas-marrov" title="Ideas, Planning, & Feedback">🤔</a> <a href="#mentoring-marrov" title="Mentoring">🧑‍🏫</a> <a href="#projectManagement-marrov" title="Project Management">📆</a> <a href="#talk-marrov" title="Talks">📢</a></td>
       <td align="center" valign="top" width="11.11%"><a href="http://marcelotryle.com"><img src="https://avatars3.githubusercontent.com/u/7353520?v=4?s=100" width="100px;" alt="Marcelo Trylesinski"/><br /><sub><b>Marcelo Trylesinski</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=Kludex" title="Documentation">📖</a></td>
@@ -225,9 +226,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/mariamjabara"><img src="?s=100" width="100px;" alt="Mariam Jabara"/><br /><sub><b>Mariam Jabara</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=mariamjabara" title="Code">💻</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://twitter.com/marielli"><img src="https://avatars2.githubusercontent.com/u/13499809?v=4?s=100" width="100px;" alt="Marielle"/><br /><sub><b>Marielle</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=marielledado" title="Documentation">📖</a> <a href="https://github.com/sktime/sktime/commits?author=marielledado" title="Code">💻</a> <a href="#ideas-marielledado" title="Ideas, Planning, & Feedback">🤔</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/mloning"><img src="https://avatars3.githubusercontent.com/u/21020482?v=4?s=100" width="100px;" alt="Markus Löning"/><br /><sub><b>Markus Löning</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=mloning" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=mloning" title="Tests">⚠️</a> <a href="#maintenance-mloning" title="Maintenance">🚧</a> <a href="#platform-mloning" title="Packaging/porting to new platform">📦</a> <a href="https://github.com/sktime/sktime/pulls?q=is%3Apr+reviewed-by%3Amloning" title="Reviewed Pull Requests">👀</a> <a href="#infra-mloning" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a> <a href="#example-mloning" title="Examples">💡</a> <a href="https://github.com/sktime/sktime/issues?q=author%3Amloning" title="Bug reports">🐛</a> <a href="#tutorial-mloning" title="Tutorials">✅</a> <a href="#business-mloning" title="Business development">💼</a> <a href="https://github.com/sktime/sktime/commits?author=mloning" title="Documentation">📖</a> <a href="#design-mloning" title="Design">🎨</a> <a href="#eventOrganizing-mloning" title="Event Organizing">📋</a> <a href="#fundingFinding-mloning" title="Funding Finding">🔍</a> <a href="#ideas-mloning" title="Ideas, Planning, & Feedback">🤔</a> <a href="#projectManagement-mloning" title="Project Management">📆</a> <a href="#question-mloning" title="Answering Questions">💬</a> <a href="#talk-mloning" title="Talks">📢</a> <a href="#mentoring-mloning" title="Mentoring">🧑‍🏫</a> <a href="#video-mloning" title="Videos">📹</a></td>
-      <td align="center" valign="top" width="11.11%"><a href="https://github.com/markussagen"><img src="https://avatars.githubusercontent.com/u/20767068?v=4?s=100" width="100px;" alt="Markus Sagen"/><br /><sub><b>Markus Sagen</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=markussagen" title="Code">💻</a> <a href="#example-markussagen" title="Examples">💡</a></td>
     </tr>
     <tr>
+      <td align="center" valign="top" width="11.11%"><a href="https://github.com/markussagen"><img src="https://avatars.githubusercontent.com/u/20767068?v=4?s=100" width="100px;" alt="Markus Sagen"/><br /><sub><b>Markus Sagen</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=markussagen" title="Code">💻</a> <a href="#example-markussagen" title="Examples">💡</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://www.linkedin.com/in/martin-walter-1a33b3114/"><img src="https://avatars0.githubusercontent.com/u/29627036?v=4?s=100" width="100px;" alt="Martin Walter"/><br /><sub><b>Martin Walter</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=aiwalter" title="Code">💻</a> <a href="https://github.com/sktime/sktime/issues?q=author%3Aaiwalter" title="Bug reports">🐛</a> <a href="#projectManagement-aiwalter" title="Project Management">📆</a> <a href="#fundingFinding-aiwalter" title="Funding Finding">🔍</a> <a href="#mentoring-aiwalter" title="Mentoring">🧑‍🏫</a> <a href="#ideas-aiwalter" title="Ideas, Planning, & Feedback">🤔</a> <a href="#design-aiwalter" title="Design">🎨</a> <a href="https://github.com/sktime/sktime/pulls?q=is%3Apr+reviewed-by%3Aaiwalter" title="Reviewed Pull Requests">👀</a> <a href="https://github.com/sktime/sktime/commits?author=aiwalter" title="Documentation">📖</a> <a href="#talk-aiwalter" title="Talks">📢</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/martinagvilas"><img src="https://avatars2.githubusercontent.com/u/37339384?v=4?s=100" width="100px;" alt="Martina G. Vilas"/><br /><sub><b>Martina G. Vilas</b></sub></a><br /><a href="https://github.com/sktime/sktime/pulls?q=is%3Apr+reviewed-by%3Amartinagvilas" title="Reviewed Pull Requests">👀</a> <a href="#ideas-martinagvilas" title="Ideas, Planning, & Feedback">🤔</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/mateuszkasprowicz"><img src="https://avatars.githubusercontent.com/mateuszkasprowicz?s=100" width="100px;" alt="Mateusz Kasprowicz"/><br /><sub><b>Mateusz Kasprowicz</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=mateuszkasprowicz" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=mateuszkasprowicz" title="Tests">⚠️</a></td>
@@ -236,9 +237,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/tvdboom"><img src="https://avatars.githubusercontent.com/u/32366550?v=4?s=100" width="100px;" alt="Mavs"/><br /><sub><b>Mavs</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=tvdboom" title="Code">💻</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://www.linkedin.com/in/maxfrohlich/"><img src="https://avatars.githubusercontent.com/u/16393653?v=4?s=100" width="100px;" alt="Max Frohlich"/><br /><sub><b>Max Frohlich</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=ninedigits" title="Code">💻</a> <a href="#ideas-ninedigits" title="Ideas, Planning, & Feedback">🤔</a> <a href="#maintenance-ninedigits" title="Maintenance">🚧</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/solen0id"><img src="https://avatars.githubusercontent.com/u/20767606?v=4?s=100" width="100px;" alt="Max Patzelt"/><br /><sub><b>Max Patzelt</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=solen0id" title="Code">💻</a></td>
-      <td align="center" valign="top" width="11.11%"><a href="https://github.com/meraldoantonio"><img src="https://avatars.githubusercontent.com/u/37468543?v=4?s=100" width="100px;" alt="Meraldo Antonio"/><br /><sub><b>Meraldo Antonio</b></sub></a><br /><a href="https://github.com/sktime/sktime/issues?q=author%3Ameraldoantonio" title="Bug reports">🐛</a> <a href="https://github.com/sktime/sktime/commits?author=meraldoantonio" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=meraldoantonio" title="Documentation">📖</a> <a href="https://github.com/sktime/sktime/commits?author=meraldoantonio" title="Tests">⚠️</a></td>
     </tr>
     <tr>
+      <td align="center" valign="top" width="11.11%"><a href="https://github.com/meraldoantonio"><img src="https://avatars.githubusercontent.com/u/37468543?v=4?s=100" width="100px;" alt="Meraldo Antonio"/><br /><sub><b>Meraldo Antonio</b></sub></a><br /><a href="https://github.com/sktime/sktime/issues?q=author%3Ameraldoantonio" title="Bug reports">🐛</a> <a href="https://github.com/sktime/sktime/commits?author=meraldoantonio" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=meraldoantonio" title="Documentation">📖</a> <a href="https://github.com/sktime/sktime/commits?author=meraldoantonio" title="Tests">⚠️</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/Hephaest"><img src="https://avatars2.githubusercontent.com/u/37981444?v=4?s=100" width="100px;" alt="Miao Cai"/><br /><sub><b>Miao Cai</b></sub></a><br /><a href="https://github.com/sktime/sktime/issues?q=author%3AHephaest" title="Bug reports">🐛</a> <a href="https://github.com/sktime/sktime/commits?author=Hephaest" title="Code">💻</a></td>
       <td align="center" valign="top" width="11.11%"><a href="michaelfeil.eu"><img src="https://avatars.githubusercontent.com/u/63565275?v=4?s=100" width="100px;" alt="Michael Feil"/><br /><sub><b>Michael Feil</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=michaelfeil" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=michaelfeil" title="Tests">⚠️</a> <a href="#ideas-michaelfeil" title="Ideas, Planning, & Feedback">🤔</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/mgazian000"><img src="https://avatars.githubusercontent.com/mgazian000?s=100" width="100px;" alt="Michael Gaziani"/><br /><sub><b>Michael Gaziani</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=mgazian000" title="Documentation">📖</a></td>
@@ -247,9 +248,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/mjste"><img src="https://avatars.githubusercontent.com/mjste?s=100" width="100px;" alt="Michał Stefanik"/><br /><sub><b>Michał Stefanik</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=mjste" title="Documentation">📖</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/mk406"><img src="https://avatars.githubusercontent.com/u/78024411?v=4?s=100" width="100px;" alt="Miguel Krause"/><br /><sub><b>Miguel Krause</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=mk406" title="Code">💻</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/miraep8"><img src="https://avatars.githubusercontent.com/u/10511777?s=400&u=10a774fd4be767fa3b23a82a98bbfe102c17f0f3&v=4?s=100" width="100px;" alt="Mirae Parker"/><br /><sub><b>Mirae Parker</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=miraep8" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=miraep8" title="Tests">⚠️</a></td>
-      <td align="center" valign="top" width="11.11%"><a href="https://github.com/MBristle"><img src="https://avatars.githubusercontent.com/MBristle?s=100" width="100px;" alt="Mirko Bristle"/><br /><sub><b>Mirko Bristle</b></sub></a><br /><a href="https://github.com/sktime/sktime/issues?q=author%3AMBristle" title="Bug reports">🐛</a> <a href="https://github.com/sktime/sktime/commits?author=MBristle" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=MBristle" title="Documentation">📖</a> <a href="https://github.com/sktime/sktime/commits?author=MBristle" title="Tests">⚠️</a></td>
     </tr>
     <tr>
+      <td align="center" valign="top" width="11.11%"><a href="https://github.com/MBristle"><img src="https://avatars.githubusercontent.com/MBristle?s=100" width="100px;" alt="Mirko Bristle"/><br /><sub><b>Mirko Bristle</b></sub></a><br /><a href="https://github.com/sktime/sktime/issues?q=author%3AMBristle" title="Bug reports">🐛</a> <a href="https://github.com/sktime/sktime/commits?author=MBristle" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=MBristle" title="Documentation">📖</a> <a href="https://github.com/sktime/sktime/commits?author=MBristle" title="Tests">⚠️</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://mo-saif.github.io/"><img src="https://avatars0.githubusercontent.com/u/27867617?v=4?s=100" width="100px;" alt="Mohammed Saif Kazamel"/><br /><sub><b>Mohammed Saif Kazamel</b></sub></a><br /><a href="https://github.com/sktime/sktime/issues?q=author%3AMo-Saif" title="Bug reports">🐛</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://moradabaz.github.io/"><img src="https://avatars.githubusercontent.com/u/29915156?v=4?s=100" width="100px;" alt="Morad :)"/><br /><sub><b>Morad :)</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=moradabaz" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=moradabaz" title="Tests">⚠️</a> <a href="https://github.com/sktime/sktime/commits?author=moradabaz" title="Documentation">📖</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/skinan"><img src="https://avatars.githubusercontent.com/skinan?s=100" width="100px;" alt="Muhammad Sakib Khan Inan"/><br /><sub><b>Muhammad Sakib Khan Inan</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=skinan" title="Documentation">📖</a></td>
@@ -258,9 +259,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/niekvanderlaan"><img src="https://avatars.githubusercontent.com/u/9962825?v=4?s=100" width="100px;" alt="Niek van der Laan"/><br /><sub><b>Niek van der Laan</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=niekvanderlaan" title="Code">💻</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/Dehelaan"><img src="https://avatars.githubusercontent.com/u/120308161?s=400&v=4?s=100" width="100px;" alt="Nihal Chaudhary"/><br /><sub><b>Nihal Chaudhary</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=Dehelaan" title="Documentation">📖</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/ngupta23"><img src="https://avatars0.githubusercontent.com/u/33585645?v=4?s=100" width="100px;" alt="Nikhil Gupta"/><br /><sub><b>Nikhil Gupta</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=ngupta23" title="Code">💻</a> <a href="https://github.com/sktime/sktime/issues?q=author%3Angupta23" title="Bug reports">🐛</a> <a href="https://github.com/sktime/sktime/commits?author=ngupta23" title="Documentation">📖</a></td>
-      <td align="center" valign="top" width="11.11%"><a href="https://www.linkedin.com/in/nshahpazov/"><img src="https://avatars.githubusercontent.com/nshahpazov?s=100" width="100px;" alt="Nikola Shahpazov"/><br /><sub><b>Nikola Shahpazov</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=nshahpazov" title="Documentation">📖</a></td>
     </tr>
     <tr>
+      <td align="center" valign="top" width="11.11%"><a href="https://www.linkedin.com/in/nshahpazov/"><img src="https://avatars.githubusercontent.com/nshahpazov?s=100" width="100px;" alt="Nikola Shahpazov"/><br /><sub><b>Nikola Shahpazov</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=nshahpazov" title="Documentation">📖</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/nilesh05apr"><img src="https://avatars.githubusercontent.com/u/65773314?v=4?s=100" width="100px;" alt="Nilesh Kumar"/><br /><sub><b>Nilesh Kumar</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=nilesh05apr" title="Code">💻</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/ninfueng"><img src="https://avatars2.githubusercontent.com/u/28499769?v=4?s=100" width="100px;" alt="Ninnart Fuengfusin"/><br /><sub><b>Ninnart Fuengfusin</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=ninfueng" title="Code">💻</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/NoaBenAmi"><img src="https://avatars.githubusercontent.com/u/37590002?v=4?s=100" width="100px;" alt="Noa Ben Ami"/><br /><sub><b>Noa Ben Ami</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=NoaBenAmi" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=NoaBenAmi" title="Tests">⚠️</a> <a href="https://github.com/sktime/sktime/commits?author=NoaBenAmi" title="Documentation">📖</a></td>
@@ -269,9 +270,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/olivermatthews"><img src="https://avatars.githubusercontent.com/u/31141490?v=4?s=100" width="100px;" alt="Oliver Matthews"/><br /><sub><b>Oliver Matthews</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=OliverMatthews" title="Code">💻</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/prockenschaub"><img src="https://avatars0.githubusercontent.com/u/15381732?v=4?s=100" width="100px;" alt="Patrick Rockenschaub"/><br /><sub><b>Patrick Rockenschaub</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=prockenschaub" title="Code">💻</a> <a href="#design-prockenschaub" title="Design">🎨</a> <a href="#ideas-prockenschaub" title="Ideas, Planning, & Feedback">🤔</a> <a href="https://github.com/sktime/sktime/commits?author=prockenschaub" title="Tests">⚠️</a></td>
       <td align="center" valign="top" width="11.11%"><a href="http://www2.informatik.hu-berlin.de/~schaefpa/"><img src="https://avatars0.githubusercontent.com/u/7783034?v=4?s=100" width="100px;" alt="Patrick Schäfer"/><br /><sub><b>Patrick Schäfer</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=patrickzib" title="Code">💻</a> <a href="#tutorial-patrickzib" title="Tutorials">✅</a></td>
-      <td align="center" valign="top" width="11.11%"><a href="https://ber.gp"><img src="https://avatars1.githubusercontent.com/u/9824244?v=4?s=100" width="100px;" alt="Paul"/><br /><sub><b>Paul</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=Pangoraw" title="Documentation">📖</a></td>
     </tr>
     <tr>
+      <td align="center" valign="top" width="11.11%"><a href="https://ber.gp"><img src="https://avatars1.githubusercontent.com/u/9824244?v=4?s=100" width="100px;" alt="Paul"/><br /><sub><b>Paul</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=Pangoraw" title="Documentation">📖</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://www.linkedin.com/in/paulyim97/"><img src="https://avatars.githubusercontent.com/pyyim?s=100" width="100px;" alt="Paul Yim"/><br /><sub><b>Paul Yim</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=pyyim" title="Code">💻</a> <a href="#example-pyyim" title="Examples">💡</a> <a href="https://github.com/sktime/sktime/commits?author=pyyim" title="Tests">⚠️</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://www.imes.uni-hannover.de/de/institut/team/m-sc-karl-philipp-kortmann/"><img src="https://avatars.githubusercontent.com/u/20466981?v=4?s=100" width="100px;" alt="Philipp Kortmann"/><br /><sub><b>Philipp Kortmann</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=MrPr3ntice" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=MrPr3ntice" title="Documentation">📖</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/Piyush1729"><img src="https://avatars2.githubusercontent.com/u/64950012?v=4?s=100" width="100px;" alt="Piyush Gade"/><br /><sub><b>Piyush Gade</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=Piyush1729" title="Code">💻</a> <a href="https://github.com/sktime/sktime/pulls?q=is%3Apr+reviewed-by%3APiyush1729" title="Reviewed Pull Requests">👀</a></td>
@@ -280,9 +281,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/Prtm2110"><img src="https://avatars.githubusercontent.com/u/139000226?s=400&u=a2ef29514ad2780d1263ba9d724898132c8d18fb&v=4?s=100" width="100px;" alt="Pratham Hole"/><br /><sub><b>Pratham Hole</b></sub></a><br /><a href="#maintenance-Prtm2110" title="Maintenance">🚧</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/pul95"><img src="https://avatars.githubusercontent.com/pul95?s=100" width="100px;" alt="Pulkit Verma"/><br /><sub><b>Pulkit Verma</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=pul95" title="Documentation">📖</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/Quaterion"><img src="https://avatars2.githubusercontent.com/u/23200273?v=4?s=100" width="100px;" alt="Quaterion"/><br /><sub><b>Quaterion</b></sub></a><br /><a href="https://github.com/sktime/sktime/issues?q=author%3AQuaterion" title="Bug reports">🐛</a></td>
-      <td align="center" valign="top" width="11.11%"><a href="https://github.com/rakshitha123"><img src="https://avatars.githubusercontent.com/u/7654679?v=4?s=100" width="100px;" alt="Rakshitha Godahewa"/><br /><sub><b>Rakshitha Godahewa</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=rakshitha123" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=rakshitha123" title="Documentation">📖</a></td>
     </tr>
     <tr>
+      <td align="center" valign="top" width="11.11%"><a href="https://github.com/rakshitha123"><img src="https://avatars.githubusercontent.com/u/7654679?v=4?s=100" width="100px;" alt="Rakshitha Godahewa"/><br /><sub><b>Rakshitha Godahewa</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=rakshitha123" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=rakshitha123" title="Documentation">📖</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/Ram0nB"><img src="https://avatars.githubusercontent.com/u/45173421?s=100" width="100px;" alt="Ramon Bussing"/><br /><sub><b>Ramon Bussing</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=Ram0nB" title="Documentation">📖</a> <a href="https://github.com/sktime/sktime/commits?author=Ram0nB" title="Code">💻</a> <a href="https://github.com/sktime/sktime/issues?q=author%3ARam0nB" title="Bug reports">🐛</a> <a href="https://github.com/sktime/sktime/commits?author=Ram0nB" title="Tests">⚠️</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/RavenRudi"><img src="https://avatars.githubusercontent.com/u/46402968?v=4?s=100" width="100px;" alt="RavenRudi"/><br /><sub><b>RavenRudi</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=RavenRudi" title="Code">💻</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/wolph"><img src="?s=100" width="100px;" alt="Rick van Hattem"/><br /><sub><b>Rick van Hattem</b></sub></a><br /><a href="#infra-wolph" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a></td>
@@ -291,9 +292,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/VectorNd"><img src="https://avatars.githubusercontent.com/u/111004091?v=4?s=100" width="100px;" alt="Rishabh Kamboj"/><br /><sub><b>Rishabh Kamboj</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=VectorNd" title="Code">💻</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/RishiKumarRay"><img src="https://avatars.githubusercontent.com/u/87641376?v=4?s=100" width="100px;" alt="Rishi Kumar Ray"/><br /><sub><b>Rishi Kumar Ray</b></sub></a><br /><a href="#infra-RishiKumarRay" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a></td>
       <td align="center" valign="top" width="11.11%"><img src="https://avatars.githubusercontent.com/u/55790848?v=4?s=100" width="100px;" alt="Riya Elizabeth John"/><br /><sub><b>Riya Elizabeth John</b></sub><br /><a href="https://github.com/sktime/sktime/commits?author=Riyabelle25" title="Code">💻</a></td>
-      <td align="center" valign="top" width="11.11%"><a href="https://github.com/Riyabelle25"><img src="https://avatars.githubusercontent.com/u/55790848?v=4?s=100" width="100px;" alt="Riya Elizabeth John"/><br /><sub><b>Riya Elizabeth John</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=Riyabelle25" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=Riyabelle25" title="Tests">⚠️</a> <a href="https://github.com/sktime/sktime/commits?author=Riyabelle25" title="Documentation">📖</a></td>
     </tr>
     <tr>
+      <td align="center" valign="top" width="11.11%"><a href="https://github.com/Riyabelle25"><img src="https://avatars.githubusercontent.com/u/55790848?v=4?s=100" width="100px;" alt="Riya Elizabeth John"/><br /><sub><b>Riya Elizabeth John</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=Riyabelle25" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=Riyabelle25" title="Tests">⚠️</a> <a href="https://github.com/sktime/sktime/commits?author=Riyabelle25" title="Documentation">📖</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://linkedin.com/in/robert-kuebler"><img src="https://avatars.githubusercontent.com/u/932327?v=4?s=100" width="100px;" alt="Robert Kübler"/><br /><sub><b>Robert Kübler</b></sub></a><br /><a href="https://github.com/sktime/sktime/issues?q=author%3AGarve" title="Bug reports">🐛</a> <a href="https://github.com/sktime/sktime/commits?author=Garve" title="Code">💻</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://www.linkedin.com/in/romanlutz/"><img src="https://avatars.githubusercontent.com/u/10245648?v=4?s=100" width="100px;" alt="Roman Lutz"/><br /><sub><b>Roman Lutz</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=romanlutz" title="Documentation">📖</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/ronnie-llamado"><img src="https://avatars.githubusercontent.com/ronnie-llamado?s=100" width="100px;" alt="Ronnie Llamado"/><br /><sub><b>Ronnie Llamado</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=ronnie-llamado" title="Documentation">📖</a></td>
@@ -302,9 +303,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/SaiRevanth25"><img src="https://avatars.githubusercontent.com/SaiRevanth25?s=100" width="100px;" alt="Sai Revanth Gowravajhala"/><br /><sub><b>Sai Revanth Gowravajhala</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=SaiRevanth25" title="Code">💻</a> <a href="https://github.com/sktime/sktime/issues?q=author%3ASaiRevanth25" title="Bug reports">🐛</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://sajay.online"><img src="https://avatars2.githubusercontent.com/u/25329624?v=4?s=100" width="100px;" alt="Sajaysurya Ganesh"/><br /><sub><b>Sajaysurya Ganesh</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=sajaysurya" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=sajaysurya" title="Documentation">📖</a> <a href="#design-sajaysurya" title="Design">🎨</a> <a href="#example-sajaysurya" title="Examples">💡</a> <a href="#ideas-sajaysurya" title="Ideas, Planning, & Feedback">🤔</a> <a href="https://github.com/sktime/sktime/commits?author=sajaysurya" title="Tests">⚠️</a> <a href="#tutorial-sajaysurya" title="Tutorials">✅</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/SamiAlavi"><img src="https://avatars.githubusercontent.com/u/32700289?v=4?s=100" width="100px;" alt="Sami Alavi"/><br /><sub><b>Sami Alavi</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=SamiAlavi" title="Code">💻</a> <a href="#maintenance-SamiAlavi" title="Maintenance">🚧</a></td>
-      <td align="center" valign="top" width="11.11%"><a href="https://github.com/SamruddhiNavale"><img src="https://avatars.githubusercontent.com/u/86359115?v=4?s=100" width="100px;" alt="Samruddhi Navale"/><br /><sub><b>Samruddhi Navale</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=SamruddhiNavale" title="Documentation">📖</a></td>
     </tr>
     <tr>
+      <td align="center" valign="top" width="11.11%"><a href="https://github.com/SamruddhiNavale"><img src="https://avatars.githubusercontent.com/u/86359115?v=4?s=100" width="100px;" alt="Samruddhi Navale"/><br /><sub><b>Samruddhi Navale</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=SamruddhiNavale" title="Documentation">📖</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/sanjayk0508"><img src="https://avatars.githubusercontent.com/u/102804548?v=4?s=100" width="100px;" alt="Sanjay Kumar"/><br /><sub><b>Sanjay Kumar</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=sanjayk0508" title="Tests">⚠️</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/deysanjeeb"><img src="https://avatars.githubusercontent.com/u/39940629?v=4?s=100" width="100px;" alt="Sanjeeb Dey"/><br /><sub><b>Sanjeeb Dey</b></sub></a><br /><a href="#maintenance-deysanjeeb" title="Maintenance">🚧</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/sssilvar"><img src="https://avatars.githubusercontent.com/u/16252054?v=4?s=100" width="100px;" alt="Santiago Smith Silva"/><br /><sub><b>Santiago Smith Silva</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=sssilvar" title="Code">💻</a></td>
@@ -313,9 +314,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
       <td align="center" valign="top" width="11.11%"><a href="https://www.linkedin.com/in/satya-pattnaik-77a430144/"><img src="https://avatars.githubusercontent.com/u/22102468?v=4?s=100" width="100px;" alt="Satya Prakash Pattnaik"/><br /><sub><b>Satya Prakash Pattnaik</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=satya-pattnaik" title="Documentation">📖</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/dasgupsa"><img src="https://avatars2.githubusercontent.com/u/10398956?v=4?s=100" width="100px;" alt="Saurabh Dasgupta"/><br /><sub><b>Saurabh Dasgupta</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=dasgupsa" title="Code">💻</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/SebasKoel"><img src="https://avatars3.githubusercontent.com/u/66252156?v=4?s=100" width="100px;" alt="Sebastiaan Koel"/><br /><sub><b>Sebastiaan Koel</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=SebasKoel" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=SebasKoel" title="Documentation">📖</a></td>
-      <td align="center" valign="top" width="11.11%"><a href="https://github.com/shagn"><img src="https://avatars.githubusercontent.com/u/16029092?v=4?s=100" width="100px;" alt="Sebastian Hagn"/><br /><sub><b>Sebastian Hagn</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=shagn" title="Documentation">📖</a></td>
     </tr>
     <tr>
+      <td align="center" valign="top" width="11.11%"><a href="https://github.com/shagn"><img src="https://avatars.githubusercontent.com/u/16029092?v=4?s=100" width="100px;" alt="Sebastian Hagn"/><br /><sub><b>Sebastian Hagn</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=shagn" title="Documentation">📖</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/bastisar"><img src="https://avatars.githubusercontent.com/u/142449680?v=4?s=100" width="100px;" alt="Sebastian Hien"/><br /><sub><b>Sebastian Hien</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=bastisar" title="Code">💻</a> <a href="https://github.com/sktime/sktime/issues?q=author%3Abastisar" title="Bug reports">🐛</a> <a href="https://github.com/sktime/sktime/commits?author=bastisar" title="Tests">⚠️</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/ShivamPathak99"><img src="https://avatars.githubusercontent.com/u/98941325?s=400&v=4?s=100" width="100px;" alt="Shivam Pathak"/><br /><sub><b>Shivam Pathak</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=ShivamPathak99" title="Documentation">📖</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/ShivamJ07"><img src="https://avatars.githubusercontent.com/ShivamJ07?s=100" width="100px;" alt="Shivam Singal"/><br /><sub><b>Shivam Singal</b></sub></a><br /><a href="https://github.com/sktime/sktime/issues?q=author%3AShivamJ07" title="Bug reports">🐛</a> <a href="https://github.com/sktime/sktime/commits?author=ShivamJ07" title="Code">💻</a></td>
@@ -324,9 +325,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/ShreeshaM07"><img src="https://avatars.githubusercontent.com/u/120820143?s=400&v=4?s=100" width="100px;" alt="Shreesha M"/><br /><sub><b>Shreesha M</b></sub></a><br /><a href="https://github.com/sktime/sktime/issues?q=author%3AShreeshaM07" title="Bug reports">🐛</a> <a href="https://github.com/sktime/sktime/commits?author=ShreeshaM07" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=ShreeshaM07" title="Tests">⚠️</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/sbuse"><img src="https://avatars.githubusercontent.com/u/24408707?v=4?s=100" width="100px;" alt="Simon B."/><br /><sub><b>Simon B.</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=sbuse" title="Code">💻</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/slavik57"><img src="https://avatars.githubusercontent.com/u/6184997?v=4?s=100" width="100px;" alt="Slava Shpitalny"/><br /><sub><b>Slava Shpitalny</b></sub></a><br /><a href="#maintenance-slavik57" title="Maintenance">🚧</a></td>
-      <td align="center" valign="top" width="11.11%"><a href="https://github.com/Sohaib-Ahmed21"><img src="https://avatars.githubusercontent.com/Sohaib-Ahmed21?s=100" width="100px;" alt="Sohaib Ahmed"/><br /><sub><b>Sohaib Ahmed</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=Sohaib-Ahmed21" title="Code">💻</a></td>
     </tr>
     <tr>
+      <td align="center" valign="top" width="11.11%"><a href="https://github.com/Sohaib-Ahmed21"><img src="https://avatars.githubusercontent.com/Sohaib-Ahmed21?s=100" width="100px;" alt="Sohaib Ahmed"/><br /><sub><b>Sohaib Ahmed</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=Sohaib-Ahmed21" title="Code">💻</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://www.linkedin.com/in/solomon-botchway-a1383821b/"><img src="https://avatars.githubusercontent.com/u/62394255?v=4?s=100" width="100px;" alt="Solomon Botchway"/><br /><sub><b>Solomon Botchway</b></sub></a><br /><a href="#maintenance-snnbotchway" title="Maintenance">🚧</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/khrapovs"><img src="https://avatars.githubusercontent.com/u/3774663?v=4?s=100" width="100px;" alt="Stanislav Khrapov"/><br /><sub><b>Stanislav Khrapov</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=khrapovs" title="Code">💻</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/steenrotsman"><img src="https://avatars.githubusercontent.com/u/78110080?s=400&v=4?s=100" width="100px;" alt="Stijn J. Rotman"/><br /><sub><b>Stijn J. Rotman</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=steenrotsman" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=steenrotsman" title="Documentation">📖</a></td>
@@ -335,9 +336,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/Taise228"><img src="https://avatars.githubusercontent.com/u/95762401?s=400&v=4?s=100" width="100px;" alt="Taisei Yamamoto"/><br /><sub><b>Taisei Yamamoto</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=Taise228" title="Code">💻</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://thayeylolu.github.io/portfolio/"><img src="https://avatars.githubusercontent.com/u/13348874?v=4?s=100" width="100px;" alt="Taiwo Owoseni"/><br /><sub><b>Taiwo Owoseni</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=thayeylolu" title="Code">💻</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/lnthach"><img src="https://avatars0.githubusercontent.com/u/7788363?v=4?s=100" width="100px;" alt="Thach Le Nguyen"/><br /><sub><b>Thach Le Nguyen</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=lnthach" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=lnthach" title="Tests">⚠️</a></td>
-      <td align="center" valign="top" width="11.11%"><a href="https://github.com/mathco-wf"><img src="https://avatars.githubusercontent.com/mathco-wf?s=100" width="100px;" alt="TheMathcompay Widget Factory Team"/><br /><sub><b>TheMathcompay Widget Factory Team</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=mathco-wf" title="Documentation">📖</a></td>
     </tr>
     <tr>
+      <td align="center" valign="top" width="11.11%"><a href="https://github.com/mathco-wf"><img src="https://avatars.githubusercontent.com/mathco-wf?s=100" width="100px;" alt="TheMathcompay Widget Factory Team"/><br /><sub><b>TheMathcompay Widget Factory Team</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=mathco-wf" title="Documentation">📖</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/tombh"><img src="https://avatars.githubusercontent.com/u/160835?s=80&v=4?s=100" width="100px;" alt="Thomas Buckley-Houston"/><br /><sub><b>Thomas Buckley-Houston</b></sub></a><br /><a href="https://github.com/sktime/sktime/issues?q=author%3Atombh" title="Bug reports">🐛</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/xxl4tomxu98"><img src="https://avatars.githubusercontent.com/u/62292177?s=40&v=4?s=100" width="100px;" alt="Tom Xu"/><br /><sub><b>Tom Xu</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=xxl4tomxu98" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=xxl4tomxu98" title="Documentation">📖</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/tpvasconcelos"><img src="https://avatars.githubusercontent.com/u/17701527?v=4?s=100" width="100px;" alt="Tomas P. de Vasconcelos"/><br /><sub><b>Tomas P. de Vasconcelos</b></sub></a><br /><a href="https://github.com/sktime/sktime/issues?q=author%3Atpvasconcelos" title="Bug reports">🐛</a> <a href="https://github.com/sktime/sktime/commits?author=tpvasconcelos" title="Code">💻</a></td>
@@ -346,9 +347,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/onyekaugochukwu"><img src="https://avatars.githubusercontent.com/u/92909501?v=4?s=100" width="100px;" alt="Ugochukwu Onyeka"/><br /><sub><b>Ugochukwu Onyeka</b></sub></a><br /><a href="#business-onyekaugochukwu" title="Business development">💼</a> <a href="https://github.com/sktime/sktime/commits?author=onyekaugochukwu" title="Documentation">📖</a> <a href="#ideas-onyekaugochukwu" title="Ideas, Planning, & Feedback">🤔</a> <a href="#mentoring-onyekaugochukwu" title="Mentoring">🧑‍🏫</a> <a href="#projectManagement-onyekaugochukwu" title="Project Management">📆</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/utsavcoding"><img src="https://avatars3.githubusercontent.com/u/55446385?v=4?s=100" width="100px;" alt="Utsav Kumar Tiwari"/><br /><sub><b>Utsav Kumar Tiwari</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=utsavcoding" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=utsavcoding" title="Documentation">📖</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/vagechirkov"><img src="https://avatars.githubusercontent.com/vagechirkov?s=100" width="100px;" alt="Valerii Chirkov"/><br /><sub><b>Valerii Chirkov</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=vagechirkov" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=vagechirkov" title="Documentation">📖</a></td>
-      <td align="center" valign="top" width="11.11%"><a href="https://github.com/vandit98"><img src="https://avatars.githubusercontent.com/u/91458535?v=4?s=100" width="100px;" alt="Vandit Tyagi"/><br /><sub><b>Vandit Tyagi</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=vandit98" title="Documentation">📖</a></td>
     </tr>
     <tr>
+      <td align="center" valign="top" width="11.11%"><a href="https://github.com/vandit98"><img src="https://avatars.githubusercontent.com/u/91458535?v=4?s=100" width="100px;" alt="Vandit Tyagi"/><br /><sub><b>Vandit Tyagi</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=vandit98" title="Documentation">📖</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/Vasudeva-bit"><img src="https://avatars.githubusercontent.com/u/70791259?v=4?s=100" width="100px;" alt="Vasudeva Kilaru"/><br /><sub><b>Vasudeva Kilaru</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=Vasudeva-bit" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=Vasudeva-bit" title="Documentation">📖</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/vedantag17"><img src="https://avatars.githubusercontent.com/u/118207011?v=4?s=100" width="100px;" alt="Vedant Agrawal"/><br /><sub><b>Vedant Agrawal</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=vedantag17" title="Documentation">📖</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/jgyfutub"><img src="https://avatars.githubusercontent.com/u/97391064?s=400&v=4?s=100" width="100px;" alt="Vedant Pandey"/><br /><sub><b>Vedant Pandey</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=jgyfutub" title="Code">💻</a></td>
@@ -357,9 +358,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/vnicholson1"><img src="?s=100" width="100px;" alt="Vincent Nicholson"/><br /><sub><b>Vincent Nicholson</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=vnicholson1" title="Code">💻</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/VyomkeshVyas"><img src="?s=100" width="100px;" alt="Vyomkesh Vyas"/><br /><sub><b>Vyomkesh Vyas</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=VyomkeshVyas" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=VyomkeshVyas" title="Documentation">📖</a> <a href="#example-VyomkeshVyas" title="Examples">💡</a> <a href="https://github.com/sktime/sktime/commits?author=VyomkeshVyas" title="Tests">⚠️</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/wayneadams"><img src="https://avatars.githubusercontent.com/u/15034841?s=400&u=d717e9945910bcc844c5e64cd56d570c6cc4e8e6&v=4?s=100" width="100px;" alt="Wayne Adams"/><br /><sub><b>Wayne Adams</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=wayneadams" title="Documentation">📖</a></td>
-      <td align="center" valign="top" width="11.11%"><a href="https://github.com/fr1ll"><img src="https://avatars.githubusercontent.com/u/29168593?v=4?s=100" width="100px;" alt="Will Sanger"/><br /><sub><b>Will Sanger</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=fr1ll" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=fr1ll" title="Documentation">📖</a></td>
     </tr>
     <tr>
+      <td align="center" valign="top" width="11.11%"><a href="https://github.com/fr1ll"><img src="https://avatars.githubusercontent.com/u/29168593?v=4?s=100" width="100px;" alt="Will Sanger"/><br /><sub><b>Will Sanger</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=fr1ll" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=fr1ll" title="Documentation">📖</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://www.linkedin.com/in/templierw/"><img src="https://github.com/templierw.png?s=100" width="100px;" alt="William Templier"/><br /><sub><b>William Templier</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=templierw" title="Documentation">📖</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/magittan"><img src="https://avatars0.githubusercontent.com/u/14024202?v=4?s=100" width="100px;" alt="William Zheng"/><br /><sub><b>William Zheng</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=magittan" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=magittan" title="Tests">⚠️</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/wirrywoo"><img src="https://avatars.githubusercontent.com/u/148647848?v=4?s=100?s=100" width="100px;" alt="Wilson Cheung"/><br /><sub><b>Wilson Cheung</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=wirrywoo" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=wirrywoo" title="Documentation">📖</a></td>
@@ -368,20 +369,21 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/yairbeer"><img src="https://avatars.githubusercontent.com/yairbeer?s=100" width="100px;" alt="Yair Beer"/><br /><sub><b>Yair Beer</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=yairbeer" title="Code">💻</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://www.linkedin.com/in/yann-hallouard/"><img src="https://avatars.githubusercontent.com/YHallouard?s=100" width="100px;" alt="Yann Hallouard"/><br /><sub><b>Yann Hallouard</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=YHallouard" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=YHallouard" title="Tests">⚠️</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/MEMEO-PRO"><img src="https://avatars.githubusercontent.com/memeo-pro?s=100" width="100px;" alt="Yash Edake"/><br /><sub><b>Yash Edake</b></sub></a><br /><a href="#maintenance-memeo-pro" title="Maintenance">🚧</a> <a href="https://github.com/sktime/sktime/issues?q=author%3Amemeo-pro" title="Bug reports">🐛</a></td>
-      <td align="center" valign="top" width="11.11%"><a href="https://github.com/YashKhare20"><img src="https://avatars.githubusercontent.com/u/92680366?s=400?s=100" width="100px;" alt="Yash Khare"/><br /><sub><b>Yash Khare</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=YashKhare20" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=YashKhare20" title="Documentation">📖</a></td>
     </tr>
     <tr>
+      <td align="center" valign="top" width="11.11%"><a href="https://github.com/YashKhare20"><img src="https://avatars.githubusercontent.com/u/92680366?s=400?s=100" width="100px;" alt="Yash Khare"/><br /><sub><b>Yash Khare</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=YashKhare20" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=YashKhare20" title="Documentation">📖</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/yashlamba"><img src="https://avatars.githubusercontent.com/u/44164398?v=4?s=100" width="100px;" alt="Yash Lamba"/><br /><sub><b>Yash Lamba</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=yashlamba" title="Code">💻</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/xuyxu"><img src="https://avatars2.githubusercontent.com/u/22359569?v=4?s=100" width="100px;" alt="Yi-Xuan Xu"/><br /><sub><b>Yi-Xuan Xu</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=xuyxu" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=xuyxu" title="Tests">⚠️</a> <a href="#maintenance-xuyxu" title="Maintenance">🚧</a> <a href="https://github.com/sktime/sktime/commits?author=xuyxu" title="Documentation">📖</a></td>
+      <td align="center" valign="top" width="11.11%"><a href="https://github.com/y-mx"><img src="https://avatars.githubusercontent.com/y-mx?s=100" width="100px;" alt="Yimeng Xie"/><br /><sub><b>Yimeng Xie</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=y-mx" title="Code">💻</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/sz85512678"><img src="https://avatars.githubusercontent.com/sz85512678?s=100" width="100px;" alt="Zhen Shao"/><br /><sub><b>Zhen Shao</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=sz85512678" title="Code">💻</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/ZiyaoWei"><img src="https://avatars.githubusercontent.com/u/940823?v=4?s=100" width="100px;" alt="Ziyao Wei"/><br /><sub><b>Ziyao Wei</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=ZiyaoWei" title="Code">💻</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/aa25desh"><img src="https://avatars1.githubusercontent.com/u/29518290?v=4?s=100" width="100px;" alt="aa25desh"/><br /><sub><b>aa25desh</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=aa25desh" title="Code">💻</a> <a href="https://github.com/sktime/sktime/issues?q=author%3Aaa25desh" title="Bug reports">🐛</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/abandus"><img src="https://avatars2.githubusercontent.com/u/46486474?v=4?s=100" width="100px;" alt="abandus"/><br /><sub><b>abandus</b></sub></a><br /><a href="#ideas-abandus" title="Ideas, Planning, & Feedback">🤔</a> <a href="https://github.com/sktime/sktime/commits?author=abandus" title="Code">💻</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/adoherty21"><img src="https://avatars.githubusercontent.com/u/52799751?s=400&v=4?s=100" width="100px;" alt="adoherty21"/><br /><sub><b>adoherty21</b></sub></a><br /><a href="https://github.com/sktime/sktime/issues?q=author%3Aadoherty21" title="Bug reports">🐛</a></td>
-      <td align="center" valign="top" width="11.11%"><a href="https://github.com/bethrice44"><img src="https://avatars.githubusercontent.com/u/11226988?v=4?s=100" width="100px;" alt="bethrice44"/><br /><sub><b>bethrice44</b></sub></a><br /><a href="https://github.com/sktime/sktime/issues?q=author%3Abethrice44" title="Bug reports">🐛</a> <a href="https://github.com/sktime/sktime/commits?author=bethrice44" title="Code">💻</a> <a href="https://github.com/sktime/sktime/pulls?q=is%3Apr+reviewed-by%3Abethrice44" title="Reviewed Pull Requests">👀</a> <a href="https://github.com/sktime/sktime/commits?author=bethrice44" title="Tests">⚠️</a></td>
-      <td align="center" valign="top" width="11.11%"><a href="https://github.com/big-o"><img src="https://avatars1.githubusercontent.com/u/1134151?v=4?s=100" width="100px;" alt="big-o"/><br /><sub><b>big-o</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=big-o" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=big-o" title="Tests">⚠️</a> <a href="#design-big-o" title="Design">🎨</a> <a href="#ideas-big-o" title="Ideas, Planning, & Feedback">🤔</a> <a href="https://github.com/sktime/sktime/pulls?q=is%3Apr+reviewed-by%3Abig-o" title="Reviewed Pull Requests">👀</a> <a href="#tutorial-big-o" title="Tutorials">✅</a> <a href="#mentoring-big-o" title="Mentoring">🧑‍🏫</a></td>
     </tr>
     <tr>
+      <td align="center" valign="top" width="11.11%"><a href="https://github.com/bethrice44"><img src="https://avatars.githubusercontent.com/u/11226988?v=4?s=100" width="100px;" alt="bethrice44"/><br /><sub><b>bethrice44</b></sub></a><br /><a href="https://github.com/sktime/sktime/issues?q=author%3Abethrice44" title="Bug reports">🐛</a> <a href="https://github.com/sktime/sktime/commits?author=bethrice44" title="Code">💻</a> <a href="https://github.com/sktime/sktime/pulls?q=is%3Apr+reviewed-by%3Abethrice44" title="Reviewed Pull Requests">👀</a> <a href="https://github.com/sktime/sktime/commits?author=bethrice44" title="Tests">⚠️</a></td>
+      <td align="center" valign="top" width="11.11%"><a href="https://github.com/big-o"><img src="https://avatars1.githubusercontent.com/u/1134151?v=4?s=100" width="100px;" alt="big-o"/><br /><sub><b>big-o</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=big-o" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=big-o" title="Tests">⚠️</a> <a href="#design-big-o" title="Design">🎨</a> <a href="#ideas-big-o" title="Ideas, Planning, & Feedback">🤔</a> <a href="https://github.com/sktime/sktime/pulls?q=is%3Apr+reviewed-by%3Abig-o" title="Reviewed Pull Requests">👀</a> <a href="#tutorial-big-o" title="Tutorials">✅</a> <a href="#mentoring-big-o" title="Mentoring">🧑‍🏫</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/bobbys-dev"><img src="https://avatars.githubusercontent.com/bobbys-dev?s=100" width="100px;" alt="bobbys"/><br /><sub><b>bobbys</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=bobbys-dev" title="Code">💻</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/brettkoonce"><img src="https://avatars2.githubusercontent.com/u/11281814?v=4?s=100" width="100px;" alt="brett koonce"/><br /><sub><b>brett koonce</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=brettkoonce" title="Documentation">📖</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/btrtts"><img src="https://avatars3.githubusercontent.com/u/66252156?v=4?s=100" width="100px;" alt="btrtts"/><br /><sub><b>btrtts</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=btrtts" title="Documentation">📖</a></td>
@@ -389,10 +391,10 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/chrisholder"><img src="https://avatars.githubusercontent.com/u/4674372?v=4?s=100" width="100px;" alt="chrisholder"/><br /><sub><b>chrisholder</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=chrisholder" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=chrisholder" title="Tests">⚠️</a> <a href="https://github.com/sktime/sktime/commits?author=chrisholder" title="Documentation">📖</a> <a href="#design-chrisholder" title="Design">🎨</a> <a href="#example-chrisholder" title="Examples">💡</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/morestart"><img src="https://avatars.githubusercontent.com/u/35556811?s=100" width="100px;" alt="ctl"/><br /><sub><b>ctl</b></sub></a><br /><a href="https://github.com/sktime/sktime/issues?q=author%3Amorestart" title="Bug reports">🐛</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/danbartl"><img src="https://avatars.githubusercontent.com/u/19947407?v=4?s=100" width="100px;" alt="danbartl"/><br /><sub><b>danbartl</b></sub></a><br /><a href="https://github.com/sktime/sktime/issues?q=author%3Adanbartl" title="Bug reports">🐛</a> <a href="https://github.com/sktime/sktime/commits?author=danbartl" title="Code">💻</a> <a href="https://github.com/sktime/sktime/pulls?q=is%3Apr+reviewed-by%3Adanbartl" title="Reviewed Pull Requests">👀</a> <a href="#talk-danbartl" title="Talks">📢</a> <a href="https://github.com/sktime/sktime/commits?author=danbartl" title="Tests">⚠️</a> <a href="#tutorial-danbartl" title="Tutorials">✅</a> <a href="#video-danbartl" title="Videos">📹</a></td>
-      <td align="center" valign="top" width="11.11%"><a href="https://github.com/hamzahiqb"><img src="https://avatars3.githubusercontent.com/u/10302415?v=4?s=100" width="100px;" alt="hamzahiqb"/><br /><sub><b>hamzahiqb</b></sub></a><br /><a href="#infra-hamzahiqb" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a></td>
-      <td align="center" valign="top" width="11.11%"><a href="https://github.com/hiqbal2"><img src="https://avatars3.githubusercontent.com/u/10302415?v=4?s=100" width="100px;" alt="hiqbal2"/><br /><sub><b>hiqbal2</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=hiqbal2" title="Documentation">📖</a></td>
     </tr>
     <tr>
+      <td align="center" valign="top" width="11.11%"><a href="https://github.com/hamzahiqb"><img src="https://avatars3.githubusercontent.com/u/10302415?v=4?s=100" width="100px;" alt="hamzahiqb"/><br /><sub><b>hamzahiqb</b></sub></a><br /><a href="#infra-hamzahiqb" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a></td>
+      <td align="center" valign="top" width="11.11%"><a href="https://github.com/hiqbal2"><img src="https://avatars3.githubusercontent.com/u/10302415?v=4?s=100" width="100px;" alt="hiqbal2"/><br /><sub><b>hiqbal2</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=hiqbal2" title="Documentation">📖</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/jesellier"><img src="https://avatars0.githubusercontent.com/u/51952076?v=4?s=100" width="100px;" alt="jesellier"/><br /><sub><b>jesellier</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=jesellier" title="Code">💻</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/jschemm"><img src="https://avatars.githubusercontent.com/u/81151346?v=4?s=100" width="100px;" alt="jschemm"/><br /><sub><b>jschemm</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=jschemm" title="Code">💻</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/kkoziara"><img src="https://avatars1.githubusercontent.com/u/4346849?v=4?s=100" width="100px;" alt="kkoziara"/><br /><sub><b>kkoziara</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=kkoziara" title="Code">💻</a> <a href="https://github.com/sktime/sktime/issues?q=author%3Akkoziara" title="Bug reports">🐛</a></td>
@@ -400,10 +402,10 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/oleskiewicz"><img src="https://avatars1.githubusercontent.com/u/5682158?v=4?s=100" width="100px;" alt="oleskiewicz"/><br /><sub><b>oleskiewicz</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=oleskiewicz" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=oleskiewicz" title="Documentation">📖</a> <a href="https://github.com/sktime/sktime/commits?author=oleskiewicz" title="Tests">⚠️</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/pabworks"><img src="https://avatars.githubusercontent.com/u/32725127?v=4?s=100" width="100px;" alt="pabworks"/><br /><sub><b>pabworks</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=pabworks" title="Code">💻</a> <a href="https://github.com/sktime/sktime/commits?author=pabworks" title="Tests">⚠️</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/xloem"><img src="?s=100" width="100px;" alt="patiently pending world peace"/><br /><sub><b>patiently pending world peace</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=xloem" title="Code">💻</a></td>
-      <td align="center" valign="top" width="11.11%"><a href="https://github.com/raishubham1"><img src="https://avatars3.githubusercontent.com/u/29356417?v=4?s=100" width="100px;" alt="raishubham1"/><br /><sub><b>raishubham1</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=raishubham1" title="Documentation">📖</a></td>
-      <td align="center" valign="top" width="11.11%"><a href="https://github.com/simone-pignotti"><img src="https://avatars1.githubusercontent.com/u/44410066?v=4?s=100" width="100px;" alt="simone-pignotti"/><br /><sub><b>simone-pignotti</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=simone-pignotti" title="Code">💻</a> <a href="https://github.com/sktime/sktime/issues?q=author%3Asimone-pignotti" title="Bug reports">🐛</a></td>
     </tr>
     <tr>
+      <td align="center" valign="top" width="11.11%"><a href="https://github.com/raishubham1"><img src="https://avatars3.githubusercontent.com/u/29356417?v=4?s=100" width="100px;" alt="raishubham1"/><br /><sub><b>raishubham1</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=raishubham1" title="Documentation">📖</a></td>
+      <td align="center" valign="top" width="11.11%"><a href="https://github.com/simone-pignotti"><img src="https://avatars1.githubusercontent.com/u/44410066?v=4?s=100" width="100px;" alt="simone-pignotti"/><br /><sub><b>simone-pignotti</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=simone-pignotti" title="Code">💻</a> <a href="https://github.com/sktime/sktime/issues?q=author%3Asimone-pignotti" title="Bug reports">🐛</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/sophijka"><img src="https://avatars2.githubusercontent.com/u/47450591?v=4?s=100" width="100px;" alt="sophijka"/><br /><sub><b>sophijka</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=sophijka" title="Documentation">📖</a> <a href="#maintenance-sophijka" title="Maintenance">🚧</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/sri1419"><img src="https://avatars2.githubusercontent.com/u/65078278?v=4?s=100" width="100px;" alt="sri1419"/><br /><sub><b>sri1419</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=sri1419" title="Code">💻</a></td>
       <td align="center" valign="top" width="11.11%"><a href="https://github.com/tensorflow-as-tf"><img src="https://avatars.githubusercontent.com/u/51345718?v=4?s=100" width="100px;" alt="tensorflow-as-tf"/><br /><sub><b>tensorflow-as-tf</b></sub></a><br /><a href="https://github.com/sktime/sktime/commits?author=tensorflow-as-tf" title="Code">💻</a></td>
diff --git a/docs/source/api_reference/forecasting.rst b/docs/source/api_reference/forecasting.rst
index a606ee914..d84765ba2 100644
--- a/docs/source/api_reference/forecasting.rst
+++ b/docs/source/api_reference/forecasting.rst
@@ -237,6 +237,7 @@ Trend forecasters
     STLForecaster
     CurveFitForecaster
     ProphetPiecewiseLinearTrendForecaster
+    SplineTrendForecaster
 
 .. currentmodule:: sktime.forecasting.statsforecast
 
diff --git a/docs/source/api_reference/tags.rst b/docs/source/api_reference/tags.rst
index 623486153..269406311 100644
--- a/docs/source/api_reference/tags.rst
+++ b/docs/source/api_reference/tags.rst
@@ -200,3 +200,4 @@ The tags below have limited use in retrieval or inspection of objects.
 
     x_inner_mtype
     y_inner_mtype
+    visual_block_kind
diff --git a/sktime/classification/deep_learning/cnn.py b/sktime/classification/deep_learning/cnn.py
index d8c37ce76..05b5128bf 100644
--- a/sktime/classification/deep_learning/cnn.py
+++ b/sktime/classification/deep_learning/cnn.py
@@ -15,6 +15,9 @@ from sktime.utils.dependencies import _check_dl_dependencies
 class CNNClassifier(BaseDeepClassifier):
     """Time Convolutional Neural Network (CNN), as described in [1]_.
 
+    Adapted from the implementation from Fawaz et. al
+    https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/cnn.py
+
     Parameters
     ----------
     n_epochs       : int, default = 2000
@@ -51,11 +54,6 @@ class CNNClassifier(BaseDeepClassifier):
           ``input_shape[0] < 60`` in the input layer, and ``"valid"`` otherwise.
         - "valid", "same", and other values are passed directly to ``Conv1D``
 
-    Notes
-    -----
-    Adapted from the implementation from Fawaz et. al
-    https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/cnn.py
-
     References
     ----------
     .. [1] Zhao et. al, Convolutional neural networks for time series classification,
@@ -75,7 +73,8 @@ class CNNClassifier(BaseDeepClassifier):
     _tags = {
         # packaging info
         # --------------
-        "authors": ["James-Large", "TonyBagnall"],
+        "authors": ["hfawaz", "James-Large"],
+        # hfawaz for dl-4-tsc
         "maintainers": ["James-Large"],
         "python_dependencies": "tensorflow",
         # estimator type handled by parent class
diff --git a/sktime/classification/deep_learning/cntc.py b/sktime/classification/deep_learning/cntc.py
index 6238ac9c7..e4404761a 100644
--- a/sktime/classification/deep_learning/cntc.py
+++ b/sktime/classification/deep_learning/cntc.py
@@ -12,6 +12,9 @@ from sktime.utils.dependencies import _check_dl_dependencies
 class CNTCClassifier(BaseDeepClassifier):
     """Contextual Time-series Neural Classifier (CNTC), as described in [1].
 
+    Adapted from the implementation from Fullah et. al
+    https://github.com/AmaduFullah/CNTC_MODEL/blob/master/cntc.ipynb
+
     Parameters
     ----------
     n_epochs       : int, default = 2000
@@ -38,11 +41,6 @@ class CNTCClassifier(BaseDeepClassifier):
     optimizer       : keras.optimizer, default=keras.optimizers.Adam(),
     metrics         : list of strings, default=["accuracy"],
 
-    Notes
-    -----
-    Adapted from the implementation from Fullah et. al
-    https://github.com/AmaduFullah/CNTC_MODEL/blob/master/cntc.ipynb
-
     References
     ----------
     .. [1] Network originally defined in:
@@ -73,7 +71,13 @@ class CNTCClassifier(BaseDeepClassifier):
     """
 
     _tags = {
-        "authors": ["James-Large", "Withington", "TonyBagnall", "AurumnPegasus"],
+        "authors": [
+            "AmaduFullah",
+            "James-Large",
+            "Withington",
+            "TonyBagnall",
+            "AurumnPegasus",
+        ],
         "maintainers": ["James-Large", "Withington", "AurumnPegasus"],
         "python_dependencies": ["tensorflow", "keras-self-attention"],
     }
diff --git a/sktime/classification/deep_learning/fcn.py b/sktime/classification/deep_learning/fcn.py
index 6a1470c33..2d0b9b5fd 100644
--- a/sktime/classification/deep_learning/fcn.py
+++ b/sktime/classification/deep_learning/fcn.py
@@ -15,6 +15,9 @@ from sktime.utils.dependencies import _check_dl_dependencies
 class FCNClassifier(BaseDeepClassifier):
     """Fully Connected Neural Network (FCN), as described in [1]_.
 
+    Adapted from the implementation from Fawaz et. al
+    https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/fcn.py
+
     Parameters
     ----------
     should inherited fields be listed here?
@@ -39,11 +42,6 @@ class FCNClassifier(BaseDeepClassifier):
     optimizer       : keras.optimizers object, default = Adam(lr=0.01)
         specify the optimizer and the learning rate to be used.
 
-    Notes
-    -----
-    Adapted from the implementation from Fawaz et. al
-    https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/fcn.py
-
     References
     ----------
     .. [1] Zhao et. al, Convolutional neural networks for time series classification,
@@ -63,7 +61,8 @@ class FCNClassifier(BaseDeepClassifier):
     _tags = {
         # packaging info
         # --------------
-        "authors": ["James-Large", "AurumnPegasus"],
+        "authors": ["hfawaz", "James-Large", "AurumnPegasus"],
+        # hfawaz for dl-4-tsc
         "maintainers": ["James-Large", "AurumnPegasus"],
         # estimator type handled by parent class
     }
diff --git a/sktime/classification/deep_learning/lstmfcn.py b/sktime/classification/deep_learning/lstmfcn.py
index 983d6c82f..e4e9aa46d 100644
--- a/sktime/classification/deep_learning/lstmfcn.py
+++ b/sktime/classification/deep_learning/lstmfcn.py
@@ -15,10 +15,8 @@ from sktime.networks.lstmfcn import LSTMFCNNetwork
 class LSTMFCNClassifier(BaseDeepClassifier):
     """Implementation of LSTMFCNClassifier from Karim et al (2019) [1].
 
-    Overview
-    --------
-     Combines an LSTM arm with a CNN arm. Optionally uses an attention mechanism in the
-     LSTM which the author indicates provides improved performance.
+    Combines an LSTM arm with a CNN arm. Optionally uses an attention mechanism in the
+    LSTM which the author indicates provides improved performance.
 
     Parameters
     ----------
diff --git a/sktime/classification/deep_learning/mcdcnn.py b/sktime/classification/deep_learning/mcdcnn.py
index a84c21e9b..d11832a66 100644
--- a/sktime/classification/deep_learning/mcdcnn.py
+++ b/sktime/classification/deep_learning/mcdcnn.py
@@ -15,6 +15,9 @@ from sktime.utils.dependencies import _check_dl_dependencies
 class MCDCNNClassifier(BaseDeepClassifier):
     """Multi Channel Deep Convolutional Neural Classifier, as described in [1]_.
 
+    Adapted from the implementation of Fawaz et. al
+    https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/mcdcnn.py
+
     Parameters
     ----------
     n_epochs : int, optional (default=120)
@@ -57,11 +60,6 @@ class MCDCNNClassifier(BaseDeepClassifier):
     random_state : int, optional (default=0)
         The seed to any random action.
 
-    Notes
-    -----
-    Adapted from the implementation of Fawaz et. al
-    https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/mcdcnn.py
-
     References
     ----------
     .. [1] Zheng et. al, Time series classification using multi-channels deep
@@ -81,7 +79,7 @@ class MCDCNNClassifier(BaseDeepClassifier):
     _tags = {
         # packaging info
         # --------------
-        "authors": ["james-large"],
+        "authors": ["hfawaz", "james-large"],
         "maintainers": ["james-large"],
         "python_dependencies": "tensorflow",
         # estimator type handled by parent class
diff --git a/sktime/classification/deep_learning/mlp.py b/sktime/classification/deep_learning/mlp.py
index e8c770116..7e6c9f7d0 100644
--- a/sktime/classification/deep_learning/mlp.py
+++ b/sktime/classification/deep_learning/mlp.py
@@ -15,6 +15,9 @@ from sktime.utils.dependencies import _check_dl_dependencies
 class MLPClassifier(BaseDeepClassifier):
     """Multi Layer Perceptron Network (MLP), as described in [1]_.
 
+    Adapted from the implementation from source code
+    https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/mlp.py
+
     Parameters
     ----------
     should inherited fields be listed here?
@@ -39,11 +42,6 @@ class MLPClassifier(BaseDeepClassifier):
     optimizer       : keras.optimizers object, default = Adam(lr=0.01)
         specify the optimizer and the learning rate to be used.
 
-    Notes
-    -----
-    Adapted from the implementation from source code
-    https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/mlp.py
-
     References
     ----------
     .. [1] Wang et. al, Time series classification from
@@ -63,7 +61,8 @@ class MLPClassifier(BaseDeepClassifier):
     _tags = {
         # packaging info
         # --------------
-        "authors": ["James-Large", "AurumnPegasus"],
+        "authors": ["hfawaz", "James-Large", "AurumnPegasus"],
+        # hfawaz for dl-4-tsc
         "maintainers": ["James-Large", "AurumnPegasus"],
         # estimator type handled by parent class
     }
diff --git a/sktime/classification/deep_learning/resnet.py b/sktime/classification/deep_learning/resnet.py
index 82e46c667..6a2a84b28 100644
--- a/sktime/classification/deep_learning/resnet.py
+++ b/sktime/classification/deep_learning/resnet.py
@@ -15,6 +15,9 @@ from sktime.utils.dependencies import _check_dl_dependencies
 class ResNetClassifier(BaseDeepClassifier):
     """Residual Neural Network as described in [1].
 
+    Adapted from the implementation from source code
+    https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/resnet.py
+
     Parameters
     ----------
     n_epochs       : int, default = 1500
@@ -38,12 +41,6 @@ class ResNetClassifier(BaseDeepClassifier):
     optimizer       : keras.optimizers object, default = Adam(lr=0.01)
         specify the optimizer and the learning rate to be used.
 
-
-    Notes
-    -----
-    Adapted from the implementation from source code
-    https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/resnet.py
-
     References
     ----------
         .. [1] Wang et. al, Time series classification from
@@ -63,7 +60,8 @@ class ResNetClassifier(BaseDeepClassifier):
     _tags = {
         # packaging info
         # --------------
-        "authors": ["James-Large", "AurumnPegasus", "nilesh05apr"],
+        "authors": ["hfawaz", "James-Large", "AurumnPegasus", "nilesh05apr"],
+        # hfawaz for dl-4-tsc
         "maintainers": ["James-Large", "AurumnPegasus", "nilesh05apr"],
         "python_dependencies": ["tensorflow"],
         # estimator type handled by parent class
diff --git a/sktime/datatypes/_panel/_check.py b/sktime/datatypes/_panel/_check.py
index f0040ac44..723056216 100644
--- a/sktime/datatypes/_panel/_check.py
+++ b/sktime/datatypes/_panel/_check.py
@@ -1184,7 +1184,58 @@ def _check_polars_panel(obj, return_metadata=False, var_name="obj", scitype="Pan
 
 
 class PanelGluontsList(ScitypePanel):
-    """Data type: polars.DataFrame based specification of panel of time series.
+    """Data type: gluonTS representation of univariate and multivariate time series.
+
+    Name: ``"gluonts_ListDataset_panel"``
+
+    Short description:
+
+    A ``list`` of ``dict``,
+    with list index = instances, ``dict['target']`` rows = time points,
+    ``dict['target']`` cols = variables, and ``dict['start']`` marking the interval.
+    Identical to ``gluonts.dataset.common.ListDataset``.
+
+    Long description:
+
+    The ``"gluonts_ListDataset_panel"`` :term:`mtype` is a concrete specification
+    that implements the ``Panel`` :term:`scitype`, i.e., the abstract
+    type of a collection of time series.
+
+    An object ``obj: list`` follows the specification iff:
+
+    * structure convention: ``obj`` must be a ``list`` of ``dict``.
+      Each ``dict`` must contain a key ``"target"``which
+      maps to a 1D ``numpy.ndarray`` for a univariate, and
+      2D ``numpy.ndarray`` for a multivariate time series.
+      Optionally, it may also contain a key ``"start"``
+      that maps to a ``pandas.Period`` object.
+      eg: ``pandas.Period("2024-01-01", freq="D")`` for a time series starting
+      on 2024-01-01 and sampled daily.
+    * instances: instances correspond to different list elements of ``obj``.
+    * instance index: the instance index of an instance is the list index at which
+      it is located in ``obj``. That is, the data at ``obj[i]``
+      correspond to observations of the instance with index ``i``.
+    * time points: rows of ``obj[i]['target']`` correspond to
+      different, distinct time points, at which
+      instance ``i`` is observed.
+    * time index: the time index is implicit and by-convention.
+      The ``j``-th element (for an integer ``j``) of instance ``i`` is interpreted
+      as an observation at the time point ``j``.
+      If ``"start"`` key is present, the time index for
+      the ``j``-th element of instance ``i`` is ``obj[i]['start'] + j``.
+    * variables: columns of ``obj[i]['target']`` correspond to different variables
+      available for instance ``i``.
+    * variable names: ``numpy`` mtypes cannot represent variable names. If required,
+      then variable names are assigned ``"value_{k}``
+      where ``k`` is the feature column index.
+
+    Capabilities:
+
+    * can represent panels of multivariate series
+    * can not represent panels of unequally spaced series
+    * can represent panels of unequally supported series
+    * can represent panels of series with different sets of variables
+    * can represent missing values
 
     Parameters
     ----------
diff --git a/sktime/datatypes/_series/_check.py b/sktime/datatypes/_series/_check.py
index 101129a21..b247af4a5 100644
--- a/sktime/datatypes/_series/_check.py
+++ b/sktime/datatypes/_series/_check.py
@@ -546,6 +546,62 @@ def _index_equally_spaced(index):
 class SeriesXarray(ScitypeSeries):
     """Data type: xarray based specification of single time series.
 
+    Name: ``xr.DataArray``
+
+    Short description:
+
+    An ``xarray.DataArray`` representing a single time series, where:
+
+    - Each row corresponds to a time point.
+    - Columns represent variables or features.
+    - Coordinates provide additional metadata for the time index and variables.
+
+    Long description:
+
+    The ``xr.DataArray`` :term:``mtype`` is a concrete specification
+    that implements the ``Series`` :term:``scitype``, i.e., the abstract
+    type for time series data.
+
+    An object ``obj: xarray.DataArray`` follows the specification iff:
+
+    * structure convention:
+
+      - ``obj`` is a 2D array-like structure with shape ``(n_timepoints, n_features)``.
+      - ``obj.coords`` must include:
+
+        - A time-like index (``dim_0``) which is either ``Int64Index``, ``RangeIndex``,
+          ``DatetimeIndex``, or ``PeriodIndex``, and it must be monotonic.
+        - A variable-like index (``dim_1``) for feature/variable names (optional).
+
+    * time index:
+
+      - The ``dim_0`` coordinate is interpreted as the time index.
+
+    * time points:
+
+      - Each row of ``obj`` represents a single time point.
+      - Rows with the same ``dim_0`` value correspond to the same time point.
+
+    * variables:
+
+      - Columns represent different variables (or features).
+      - Column names are stored in ``dim_1`` if present.
+
+    * variable names:
+
+      - The variable names are the column names (``dim_1``), if present.
+
+    * metadata:
+
+      - Additional metadata (e.g., attributes) may be included in ``obj.attrs``.
+
+    Capabilities:
+
+    * can represent univariate or multivariate time series
+    * requires equally spaced time points (if time index is specified)
+    * supports missing values
+    * cannot represent series with differing sets of variables
+
     Parameters
     ----------
     is_univariate: bool
diff --git a/sktime/datatypes/_table/_check.py b/sktime/datatypes/_table/_check.py
index 4517e7338..bfd24c776 100644
--- a/sktime/datatypes/_table/_check.py
+++ b/sktime/datatypes/_table/_check.py
@@ -45,7 +45,32 @@ PRIMITIVE_TYPES = (float, int, str)
 
 
 class TablePdDataFrame(ScitypeTable):
-    """Data type: pandas.DataFrame based specification of data frame table.
+    """Data type: pandas.DataFrame based specification of tabular data.
+
+    Name: ``"pd_DataFrame_Table"``
+
+    Short description:
+
+    a pandas ``DataFrame`` representing tabular data,
+    with rows = instances, cols = features
+
+    Long description:
+
+    The ``"pd_DataFrame_Table"`` :term:`mtype` is a concrete specification
+    that implements the ``Table`` :term:`scitype`, i.e., the abstract
+    type of tabular data.
+
+    An object ``obj: pandas.DataFrame`` follows the specification iff:
+
+    * structure convention: ``obj.index`` can be any valid pandas index.
+    * features: columns of ``obj`` correspond to different features
+    * feature names: column names ``obj.columns``
+    * instances: rows of ``obj`` correspond to different instances
+
+    Capabilities:
+
+    * can represent multivariate data
+    * can represent missing values
 
     Parameters are inferred by check.
 
@@ -139,7 +164,32 @@ def _check_pddataframe_table(obj, return_metadata=False, var_name="obj"):
 
 
 class TablePdSeries(ScitypeTable):
-    """Data type: pandas.Series based specification of data frame table.
+    """Data type: pandas.Series based specification of tabular data.
+
+    Name: ``"pd_Series_Table"``
+
+    Short description:
+
+    a pandas ``Series`` representing tabular data,
+    with rows = instances, single feature
+
+    Long description:
+
+    The ``"pd_Series_Table"`` :term:`mtype` is a concrete specification
+    that implements the ``Table`` :term:`scitype`, i.e., the abstract
+    type of tabular data.
+
+    An object ``obj: pandas.Series`` follows the specification iff:
+
+    * structure convention: ``obj.index`` can be any valid pandas index.
+    * feature: the series ``obj`` represents a single feature
+    * feature name: the ``name`` attribute of the ``pd.Series`` object
+    * instances: rows of ``obj`` correspond to different instances
+
+    Capabilities:
+
+    * cannot represent multivariate data
+    * can represent missing values
 
     Parameters are inferred by check.
 
diff --git a/sktime/detection/_datatypes/__init__.py b/sktime/detection/_datatypes/__init__.py
new file mode 100644
index 000000000..95f3aca30
--- /dev/null
+++ b/sktime/detection/_datatypes/__init__.py
@@ -0,0 +1,1 @@
+"""Utilities to handle checks and conversions between output formats of detectors."""
diff --git a/sktime/detection/_datatypes/_check.py b/sktime/detection/_datatypes/_check.py
new file mode 100644
index 000000000..b3b1c3746
--- /dev/null
+++ b/sktime/detection/_datatypes/_check.py
@@ -0,0 +1,59 @@
+"""Utilities to handle checks and conversions between output formats of detectors."""
+
+import pandas as pd
+
+
+def _is_valid_detection(obj, type="points"):
+    """Check if the input is valid common output format.
+
+    Parameters
+    ----------
+    obj : pd.DataFrame
+        Output of a detector to check for validity
+    type : str {"points", "segments"}
+        Type of output to check for validity
+    """
+    if not isinstance(obj, pd.DataFrame):
+        return False
+    if not isinstance(obj.index, pd.RangeIndex):
+        return False
+    if "ilocs" not in obj.columns:
+        return False
+
+    if type == "points":
+        return _is_points_dtype(obj)
+    elif type == "segments":
+        return _is_segments_dtype(obj)
+
+    return False
+
+
+def _is_points_dtype(obj):
+    """Check if the input is points-like.
+
+    Assumes validity of input is checked elsewhere.
+
+    Parameters
+    ----------
+    obj : pd.DataFrame
+        Output of a detector to check
+    """
+    return pd.api.types.is_integer_dtype(obj.ilocs.dtype)
+
+
+def _is_segments_dtype(obj):
+    """Check if the input is valid segments-like.
+
+    Assumes validity of input is checked elsewhere.
+
+    Parameters
+    ----------
+    obj : pd.DataFrame
+        Output of a detector to check
+    """
+    ilocs_dtype = obj.ilocs.dtype
+    if not (isinstance(ilocs_dtype, pd.IntervalDtype) or len(obj) == 0):
+        return False
+    if not len(obj) == 0:
+        return pd.api.types.is_integer_dtype(ilocs_dtype.subtype)
+    return True
diff --git a/sktime/detection/_datatypes/_convert.py b/sktime/detection/_datatypes/_convert.py
new file mode 100644
index 000000000..3d6f1c8ef
--- /dev/null
+++ b/sktime/detection/_datatypes/_convert.py
@@ -0,0 +1,80 @@
+"""Utilities to handle checks and conversions between output formats of detectors."""
+
+import numpy as np
+import pandas as pd
+
+
+def _convert_points_to_segments(points_df, len_X=None, include_labels=False):
+    """Convert points-like output to segments-like output.
+
+    Parameters
+    ----------
+    points_df : pd.DataFrame
+        Points-like output of a detector
+    len_X : int, optional, default=None
+        Length of the input data.
+        If None, the last segment is assumed to be length 1.
+    include_labels : bool, optional, default=False
+        Whether to include labels in the output.
+        If True, labels are RangeIndex of the same length as the segments.
+
+    Returns
+    -------
+    pd.DataFrame
+        Segments-like output of a detector
+    """
+    if len(points_df) == 0:
+        return pd.DataFrame(columns=["ilocs", "labels"], dtype="int64")
+
+    points = points_df.ilocs.values
+    if points[0] != 0:
+        points = np.insert(points, 0, 0)
+    if len_X is None:
+        points = np.append(points, points[-1] + 1)
+    elif points[-1] != len_X:
+        points = np.append(points, len_X)
+
+    ilocs = pd.IntervalIndex.from_breaks(points, closed="left")
+
+    df_dict = {"ilocs": ilocs}
+    if include_labels:
+        labels = pd.RangeIndex(len(ilocs))
+        df_dict["labels"] = labels
+
+    return pd.DataFrame(df_dict)
+
+
+def _convert_segments_to_points(seg_df, len_X=None):
+    """Convert points-like output to segments-like output.
+
+    Parameters
+    ----------
+    seg_df : pd.DataFrame
+        Points-like output of a detector
+    len_X : int, optional
+        Length of the input data.
+        If None, the (right point) end of the last segment is omitted.
+
+    Returns
+    -------
+    pd.DataFrame
+        Segments-like output of a detector
+    """
+    if len(seg_df) == 0:
+        return pd.DataFrame(columns=["ilocs"], dtype="int64")
+
+    ix = seg_df.set_index("ilocs").index
+    vals = np.array([ix.left.values, ix.right.values]).transpose().flatten()
+    vals = np.unique(vals)
+
+    if not ix.is_non_overlapping_monotonic:
+        vals = np.sort(vals)
+
+    if vals[0] == 0:
+        vals = vals[1:]
+
+    if len_X is None or vals[-1] == len_X:
+        vals = vals[:-1]
+
+    points_df = pd.DataFrame({"ilocs": vals})
+    return points_df
diff --git a/sktime/detection/_datatypes/_examples.py b/sktime/detection/_datatypes/_examples.py
new file mode 100644
index 000000000..7f74c22f1
--- /dev/null
+++ b/sktime/detection/_datatypes/_examples.py
@@ -0,0 +1,68 @@
+"""Utilities to handle checks and conversions between output formats of detectors."""
+
+import pandas as pd
+
+
+def _get_example_segments_0():
+    """Generate example 0 for segmentation output.
+
+    - non-overlapping
+    - non-exhaustive
+    - no labels
+    """
+    segs = pd.IntervalIndex.from_tuples([(1, 2), (3, 6), (7, 10)], "left")
+    return pd.DataFrame({"ilocs": segs})
+
+
+def _get_example_segments_1():
+    """Generate example 1 for segmentation output.
+
+    - non-overlapping
+    - non-exhaustive
+    - labeled
+    """
+    segs = pd.IntervalIndex.from_tuples([(1, 2), (3, 6), (7, 10), (12, 14)], "left")
+    labels = pd.Series([0, 1, 1, 0], index=segs)
+    return pd.DataFrame({"ilocs": segs, "labels": labels})
+
+
+def _get_example_segments_2():
+    """Generate example 2 for segmentation output.
+
+    - non-overlapping
+    - exhaustive
+    - labeled
+    """
+    segs = pd.IntervalIndex.from_tuples([(0, 2), (2, 3), (3, 7), (7, 10)], "left")
+    labels = pd.Series([0, 1, 2, 3])
+    return pd.DataFrame({"ilocs": segs, "labels": labels})
+
+
+def _get_example_points_2():
+    """Generate example 2 for point output.
+
+    Corresponds to example 2 for segmentation output.
+    """
+    points = pd.Series([2, 3, 7], name="ilocs")
+    return pd.DataFrame({"ilocs": points})
+
+
+def _get_example_segments_3():
+    """Generate example 3 for segmentation output.
+
+    - overlapping
+    - exhaustive
+    - labeled
+    """
+    segs = pd.IntervalIndex.from_tuples([(0, 3), (2, 5), (3, 7), (7, 10)], "left")
+    labels = pd.Series([0, 1, 2, 3])
+    return pd.DataFrame({"ilocs": segs, "labels": labels})
+
+
+def _get_example_points_3():
+    """Generate example 3 as points only.
+
+    Corresponds to example 3 for point output.
+    """
+    pts = [2, 3, 5, 7]
+    return pd.DataFrame({"ilocs": pts})
diff --git a/sktime/detection/base/_base.py b/sktime/detection/base/_base.py
index d4cbe125c..b38940f29 100644
--- a/sktime/detection/base/_base.py
+++ b/sktime/detection/base/_base.py
@@ -921,18 +921,16 @@ class BaseDetector(BaseEstimator):
         9    1
         dtype: int64
         """
-        if isinstance(y_sparse, pd.DataFrame):
-            y_sparse = y_sparse.iloc[:, 0]
-        if not isinstance(y_sparse, pd.Series):
-            y_sparse = pd.Series(y_sparse, dtype="int64")
-        if isinstance(y_sparse.index.dtype, pd.IntervalDtype):
-            # Segmentation case
-            y_dense = BaseDetector._sparse_segments_to_dense(y_sparse, index)
-            return y_dense
-        else:
+        if not isinstance(y_sparse, pd.DataFrame):
+            y_sparse = pd.DataFrame(y_sparse, dtype="int64")
+        if not hasattr(y_sparse, "ilocs") or y_sparse.ilocs.dtype != "interval":
             # Anomaly/changepoint detection case
             y_dense = BaseDetector._sparse_points_to_dense(y_sparse, index)
             return y_dense
+        else:
+            # Segmentation case
+            y_dense = BaseDetector._sparse_segments_to_dense(y_sparse, index)
+            return y_dense
 
     @staticmethod
     def _sparse_points_to_dense(y_sparse, index):
@@ -952,7 +950,7 @@ class BaseDetector(BaseEstimator):
             y_sparse and 0 otherwise.
         """
         y_dense = pd.Series(np.zeros(len(index)), index=index, dtype="int64")
-        y_dense[y_sparse.values] = 1
+        y_dense[y_sparse.values.flatten()] = 1
         return y_dense
 
     @staticmethod
@@ -974,24 +972,26 @@ class BaseDetector(BaseEstimator):
             according to ``y_sparse``. Indexes that do not fall within any index are
             labelled -1.
         """
-        if y_sparse.index.is_overlapping:
+        if len(y_sparse) == 0:
+            return pd.DataFrame(0, index=index, dtype="int64", columns=["labels"])
+
+        seg_index = y_sparse.set_index("ilocs").index
+        index_rg = pd.RangeIndex(len(index))
+
+        if seg_index.is_overlapping:
             raise NotImplementedError(
                 "Cannot convert overlapping segments to a dense format yet."
             )
 
-        interval_indexes = y_sparse.index.get_indexer(index)
-
-        # Negative indexes do not fall within any interval so they are ignored
-        interval_labels = y_sparse.iloc[
-            interval_indexes[interval_indexes >= 0]
-        ].to_numpy()
-
-        # -1 is used to represent points do not fall within a segment
-        labels_dense = interval_indexes.copy()
-        labels_dense[labels_dense >= 0] = interval_labels
+        interval_ixs = seg_index.get_indexer(index_rg)
 
-        y_dense = pd.Series(labels_dense, index=index)
-        return y_dense
+        if "labels" not in y_sparse.columns:
+            y_dense = pd.DataFrame({"labels": interval_ixs}, index=index_rg)
+            return y_dense
+        else:
+            y_dense = y_sparse.labels.loc[interval_ixs]
+            y_dense = y_dense.reset_index(drop=True)
+            return pd.DataFrame({"labels": y_dense}, index=index_rg)
 
     @staticmethod
     def dense_to_sparse(y_dense):
@@ -1049,7 +1049,7 @@ class BaseDetector(BaseEstimator):
         pd.Series
             An empty series with a RangeIndex.
         """
-        return pd.Series(index=pd.RangeIndex(0), dtype="int64")
+        return pd.DataFrame(index=pd.RangeIndex(0), dtype="int64", columns=["ilocs"])
 
     @staticmethod
     def _empty_segments():
@@ -1060,7 +1060,13 @@ class BaseDetector(BaseEstimator):
         pd.Series
             An empty series with an IntervalIndex.
         """
-        return pd.Series(index=pd.IntervalIndex([]), dtype="int64")
+        empty_segs = pd.DataFrame(
+            pd.IntervalIndex([]),
+            index=pd.RangeIndex(0),
+            dtype="int64",
+            columns=["ilocs"],
+        )
+        return empty_segs
 
     @staticmethod
     def change_points_to_segments(y_sparse, start=None, end=None):
@@ -1156,7 +1162,7 @@ class BaseDetector(BaseEstimator):
         """
         if len(y_sparse) == 0:
             return BaseDetector._empty_sparse()
-        change_points = pd.Series(y_sparse.index.left)
+        change_points = y_sparse.set_index("ilocs").index.left
         return change_points
 
 
diff --git a/sktime/detection/hmm.py b/sktime/detection/hmm.py
index ce4912f07..26f9497a0 100644
--- a/sktime/detection/hmm.py
+++ b/sktime/detection/hmm.py
@@ -11,6 +11,7 @@ import numpy as np
 from scipy.stats import norm
 
 from sktime.detection.base import BaseDetector
+from sktime.detection.utils._arr_to_seg import arr_to_seg
 
 __author__ = ["miraep8"]
 __all__ = ["HMM"]
@@ -403,9 +404,11 @@ class HMM(BaseDetector):
 
         self.trans_prob = trans_prob
         self.trans_id = trans_id
-        return self._hmm_viterbi_label(
+        labels = self._hmm_viterbi_label(
             self.num_obs, self.states, self.trans_prob, self.trans_id
         )
+        y_seg = arr_to_seg(labels)
+        return y_seg
 
     @classmethod
     def get_test_params(cls, parameter_set="default"):
diff --git a/sktime/detection/hmm_learn/base.py b/sktime/detection/hmm_learn/base.py
index 1354fdf72..bebb4bd92 100644
--- a/sktime/detection/hmm_learn/base.py
+++ b/sktime/detection/hmm_learn/base.py
@@ -11,6 +11,7 @@ import numpy as np
 import pandas as pd
 
 from sktime.detection.base import BaseDetector
+from sktime.detection.utils._arr_to_seg import arr_to_seg
 
 __author__ = ["miraep8"]
 __all__ = ["BaseHMMLearn"]
@@ -93,11 +94,10 @@ class BaseHMMLearn(BaseDetector):
         annotated_x : array-like, shape = [num_observations]
             Array of predicted class labels, same size as input.
         """
-        X, series, index = self._fix_input(X)
+        X, _, _ = self._fix_input(X)
         X_prime = self._hmm_estimator.predict(X)
-        if series:
-            X_prime = pd.Series(X_prime, index=index)
-        return X_prime
+
+        return arr_to_seg(X_prime)
 
     def sample(self, n_samples=1, random_state=None, currstate=None):
         """Interface class which allows users to sample from their HMM."""
diff --git a/sktime/detection/hmm_learn/gaussian.py b/sktime/detection/hmm_learn/gaussian.py
index a0361b9b0..24147f7fe 100644
--- a/sktime/detection/hmm_learn/gaussian.py
+++ b/sktime/detection/hmm_learn/gaussian.py
@@ -19,6 +19,7 @@ class GaussianHMM(BaseHMMLearn):
         Number of states
     covariance_type : {"spherical", "diag", "full", "tied"}, optional
         The type of covariance parameters to use:
+
         * "spherical" --- each state uses a single variance value that
             applies to all features.
         * "diag" --- each state uses a diagonal covariance matrix
@@ -28,6 +29,7 @@ class GaussianHMM(BaseHMMLearn):
         * "tied" --- all mixture components of each state use **the same**
             full covariance matrix (note that this is not the same as for
             ``GaussianHMM``).
+
     min_covar : float, optional
         Floor on the diagonal of the covariance matrix to prevent
         overfitting. Defaults to 1e-3.
@@ -84,6 +86,7 @@ class GaussianHMM(BaseHMMLearn):
     covars_ : array
         Covariance parameters for each state.
         The shape depends on :attr:`covariance_type`:
+
         * (n_components, )                        if "spherical",
         * (n_components, n_features)              if "diag",
         * (n_components, n_features, n_features)  if "full",
@@ -91,8 +94,8 @@ class GaussianHMM(BaseHMMLearn):
 
     Examples
     --------
-    >>> from sktime.annotation.hmm_learn import GaussianHMM # doctest: +SKIP
-    >>> from sktime.annotation.datagen import piecewise_normal # doctest: +SKIP
+    >>> from sktime.detection.hmm_learn import GaussianHMM # doctest: +SKIP
+    >>> from sktime.detection.datagen import piecewise_normal # doctest: +SKIP
     >>> data = piecewise_normal( # doctest: +SKIP
     ...    means=[2, 4, 1], lengths=[10, 35, 40], random_state=7
     ...    ).reshape((-1, 1))
@@ -191,10 +194,15 @@ class GaussianHMM(BaseHMMLearn):
         -------
         params : dict or list of dict
         """
-        params = {
+        params0 = {
             "n_components": 3,
             "covariance_type": "diag",
             "min_covar": 1e-3,
         }
+        params1 = {
+            "n_components": 2,
+            "covariance_type": "full",
+            "min_covar": 1e-2,
+        }
 
-        return params
+        return [params0, params1]
diff --git a/sktime/detection/hmm_learn/gmm.py b/sktime/detection/hmm_learn/gmm.py
index ccd88c3ed..70f3a07b0 100644
--- a/sktime/detection/hmm_learn/gmm.py
+++ b/sktime/detection/hmm_learn/gmm.py
@@ -21,6 +21,7 @@ class GMMHMM(BaseHMMLearn):
         Number of states in the GMM.
     covariance_type : {"spherical", "diag", "full", "tied"}, optional
         The type of covariance parameters to use:
+
         * "spherical" --- each state uses a single variance value that
             applies to all features.
         * "diag" --- each state uses a diagonal covariance matrix
@@ -30,6 +31,7 @@ class GMMHMM(BaseHMMLearn):
         * "tied" --- all mixture components of each state use **the same**
             full covariance matrix (note that this is not the same as for
             ``GaussianHMM``).
+
     min_covar : float, optional
         Floor on the diagonal of the covariance matrix to prevent
         overfitting. Defaults to 1e-3.
@@ -90,6 +92,7 @@ class GMMHMM(BaseHMMLearn):
     covars_ : array
         Covariance parameters for each mixture components in each state.
         The shape depends on :attr:`covariance_type`:
+
         * (n_components, n_mix)                          if "spherical",
         * (n_components, n_mix, n_features)              if "diag",
         * (n_components, n_mix, n_features, n_features)  if "full"
@@ -97,14 +100,14 @@ class GMMHMM(BaseHMMLearn):
 
     Examples
     --------
-    >>> from sktime.annotation.hmm_learn import GMMHMM # doctest: +SKIP
-    >>> from sktime.annotation.datagen import piecewise_normal # doctest: +SKIP
-    >>> data = piecewise_normal( # doctest: +SKIP
+    >>> from sktime.detection.hmm_learn import GMMHMM
+    >>> from sktime.detection.datagen import piecewise_normal # doctest: +SKIP
+    >>> data = piecewise_normal(
     ...    means=[2, 4, 1], lengths=[10, 35, 40], random_state=7
     ...    ).reshape((-1, 1))
-    >>> model = GMMHMM(n_components=3) # doctest: +SKIP
-    >>> model = model.fit(data) # doctest: +SKIP
-    >>> labeled_data = model.predict(data) # doctest: +SKIP
+    >>> model = GMMHMM(n_components=3)
+    >>> model = model.fit(data)
+    >>> labeled_data = model.predict(data)
     """
 
     def __init__(
diff --git a/sktime/detection/hmm_learn/poisson.py b/sktime/detection/hmm_learn/poisson.py
index 453f20862..8b5b74e85 100644
--- a/sktime/detection/hmm_learn/poisson.py
+++ b/sktime/detection/hmm_learn/poisson.py
@@ -64,14 +64,14 @@ class PoissonHMM(BaseHMMLearn):
 
     Examples
     --------
-    >>> from sktime.annotation.hmm_learn import PoissonHMM # doctest: +SKIP
-    >>> from sktime.annotation.datagen import piecewise_poisson # doctest: +SKIP
-    >>> data = piecewise_poisson( # doctest: +SKIP
+    >>> from sktime.detection.hmm_learn import PoissonHMM
+    >>> from sktime.detection.datagen import piecewise_poisson
+    >>> data = piecewise_poisson(
     ...    lambdas=[1, 2, 3], lengths=[2, 4, 8], random_state=7
     ...    ).reshape((-1, 1))
-    >>> model = PoissonHMM(n_components=3) # doctest: +SKIP
-    >>> model = model.fit(data) # doctest: +SKIP
-    >>> labeled_data = model.predict(data) # doctest: +SKIP
+    >>> model = PoissonHMM(n_components=3)
+    >>> model = model.fit(data)
+    >>> labeled_data = model.predict(data)
     """
 
     _tags = {
@@ -157,8 +157,12 @@ class PoissonHMM(BaseHMMLearn):
         -------
         params : dict or list of dict
         """
-        params = {
+        params0 = {
             "n_components": 3,
         }
+        params1 = {
+            "n_components": 5,
+            "algorithm": "map",
+        }
 
-        return params
+        return [params0, params1]
diff --git a/sktime/detection/utils/__init__.py b/sktime/detection/utils/__init__.py
new file mode 100644
index 000000000..9673cc30b
--- /dev/null
+++ b/sktime/detection/utils/__init__.py
@@ -0,0 +1,1 @@
+"""Utilities for detection tasks."""
diff --git a/sktime/detection/utils/_arr_to_seg.py b/sktime/detection/utils/_arr_to_seg.py
new file mode 100644
index 000000000..fdbc10dd5
--- /dev/null
+++ b/sktime/detection/utils/_arr_to_seg.py
@@ -0,0 +1,51 @@
+"""Utilities for detection tasks."""
+
+import numpy as np
+import pandas as pd
+
+
+def arr_to_seg(arr):
+    """Convert 1D array-like of integers to segmentation data frame.
+
+    Returns a DataFrame with two columns:
+
+    - ilocs: IntervalIndex with start and end of consecutive blocks of integers.
+    - segments: The value in the block.
+
+    Parameters
+    ----------
+    arr (array-like) 1D array-like of integers
+        the array to convert to a segmentation DataFrame.
+        In "dense" format as returned by ``transform`` methods.
+
+    Returns
+    -------
+    df: pd.DataFrame
+        DataFrame with columns 'ilocs' and 'segments',
+        as returned by ``predict_segments`` methods.
+
+    Examples
+    --------
+    >>> from sktime.detection.utils import arr_to_seg
+    >>> arr = [1, 1, 2, 2, 2, 3, 4, 4]
+    >>> df = arr_to_seg(arr)
+    """
+    arr = np.asarray(arr)
+    if arr.ndim != 1:
+        raise ValueError("Input must be a 1D array-like of integers.")
+
+    # Identify block boundaries
+    diffs = np.diff(arr)
+    block_starts = np.where(diffs != 0)[0] + 1
+    block_indices = np.split(np.arange(len(arr)), block_starts)
+
+    # Create intervals and segments
+    intervals = [
+        pd.Interval(block[0], block[-1] + 1, closed="left") for block in block_indices
+    ]
+    segments = [arr[block[0]] for block in block_indices]
+
+    # Create DataFrame
+    df = pd.DataFrame({"ilocs": pd.IntervalIndex(intervals), "labels": segments})
+
+    return df
diff --git a/sktime/forecasting/base/_meta.py b/sktime/forecasting/base/_meta.py
index 740541659..72c6d8dad 100644
--- a/sktime/forecasting/base/_meta.py
+++ b/sktime/forecasting/base/_meta.py
@@ -18,6 +18,12 @@ class _HeterogenousEnsembleForecaster(_HeterogenousMetaEstimator, BaseForecaster
     # this must be an iterable of (name: str, estimator, ...) tuples for the default
     _steps_attr = "forecasters"
 
+    # if the estimator is fittable, _HeterogenousMetaEstimator also
+    # provides an override for get_fitted_params for params from the fitted estimators
+    # the fitted estimators should be in a different attribute, _steps_fitted_attr
+    # this must be an iterable of (name: str, estimator, ...) tuples for the default
+    _steps_fitted_attr = "forecasters_"
+
     def __init__(self, forecasters, n_jobs=None):
         self.forecasters = forecasters
         self.forecasters_ = None
diff --git a/sktime/forecasting/compose/_hierarchy_ensemble.py b/sktime/forecasting/compose/_hierarchy_ensemble.py
index 9a7d1f7da..131912399 100644
--- a/sktime/forecasting/compose/_hierarchy_ensemble.py
+++ b/sktime/forecasting/compose/_hierarchy_ensemble.py
@@ -61,19 +61,20 @@ class HierarchyEnsembleForecaster(_HeterogenousEnsembleForecaster):
     >>> from sktime.forecasting.trend import PolynomialTrendForecaster, TrendForecaster
     >>> from sktime.utils._testing.hierarchical import _bottom_hier_datagen
     >>> y = _bottom_hier_datagen(
-    ...         no_bottom_nodes=7,
-    ...         no_levels=2,
-    ...         random_seed=123
+    ...     no_bottom_nodes=7,
+    ...     no_levels=2,
+    ...     random_seed=123,
     ... )
 
     >>> # Example of by = 'level'
     >>> forecasters = [
     ...     ('naive', NaiveForecaster(), 0),
-    ...     ('trend', TrendForecaster(), 1)
+    ...     ('trend', TrendForecaster(), 1),
     ... ]
     >>> forecaster = HierarchyEnsembleForecaster(
-    ...                 forecasters=forecasters,
-    ...                 by='level', default = PolynomialTrendForecaster(degree=2)
+    ...     forecasters=forecasters,
+    ...     by='level',
+    ...     default=PolynomialTrendForecaster(degree=2),
     ... )
     >>> forecaster.fit(y, fh=[1, 2, 3])
     HierarchyEnsembleForecaster(...)
@@ -188,11 +189,12 @@ class HierarchyEnsembleForecaster(_HeterogenousEnsembleForecaster):
         x = X
 
         # check forecasters
-        self.forecasters_ = self._check_forecasters(y, z)
+        forecasters_ = self._check_forecasters(y, z)
+        self.forecasters_ = self._ensure_clone(forecasters_)
         self.fitted_list = []
 
         if y.index.nlevels == 1:
-            frcstr = self.forecasters_[0][1].clone()
+            frcstr = self.forecasters_[0][1]
             frcstr.fit(y, fh=fh, X=X)
             self.fitted_list.append([frcstr, y.index])
             return self
@@ -201,7 +203,7 @@ class HierarchyEnsembleForecaster(_HeterogenousEnsembleForecaster):
             hier_dict = self._get_hier_dict(z)
             for _, forecaster, level in self.forecasters_:
                 if level in hier_dict.keys():
-                    frcstr = forecaster.clone()
+                    frcstr = forecaster
                     df = z[z.index.droplevel(-1).isin(hier_dict[level])]
                     if X is not None:
                         x = X.loc[df.index]
@@ -211,7 +213,7 @@ class HierarchyEnsembleForecaster(_HeterogenousEnsembleForecaster):
         else:
             node_dict, frcstr_dict = self._get_node_dict(z)
             for key, nodes in node_dict.items():
-                frcstr = frcstr_dict[key].clone()
+                frcstr = frcstr_dict[key]
                 df = z[z.index.droplevel(-1).isin(nodes)]
                 if X is not None:
                     x = X.loc[df.index]
@@ -594,6 +596,21 @@ class HierarchyEnsembleForecaster(_HeterogenousEnsembleForecaster):
 
         return forecasters
 
+    def _ensure_clone(self, forecasters):
+        """Ensure that forecasters are cloned.
+
+        This should be done by _check_forecasters, but the function is hard to
+        understand and debug. This function is a safety net to ensure that
+        all forecasters are cloned.
+
+        This function, and _check_forecasters, should be refactored to be more
+        readable and maintainable.
+        """
+        forecasters_ = []
+        for name, forecaster, level_nd in forecasters:
+            forecasters_.append((name, forecaster.clone(), level_nd))
+        return forecasters_
+
     @classmethod
     def get_test_params(cls):
         """Return testing parameter settings for the estimator.
diff --git a/sktime/forecasting/trend/__init__.py b/sktime/forecasting/trend/__init__.py
index 70703a832..6d362609d 100644
--- a/sktime/forecasting/trend/__init__.py
+++ b/sktime/forecasting/trend/__init__.py
@@ -5,9 +5,11 @@
 __all__ = [
     "TrendForecaster",
     "PolynomialTrendForecaster",
+    "SplineTrendForecaster",
     "STLForecaster",
     "CurveFitForecaster",
     "ProphetPiecewiseLinearTrendForecaster",
+    "SplineTrendForecaster",
 ]
 
 from sktime.forecasting.trend._curve_fit_forecaster import CurveFitForecaster
@@ -17,5 +19,8 @@ from sktime.forecasting.trend._polynomial_trend_forecaster import (
 from sktime.forecasting.trend._pwl_trend_forecaster import (
     ProphetPiecewiseLinearTrendForecaster,
 )
+from sktime.forecasting.trend._spline_trend_forecaster import (
+    SplineTrendForecaster,
+)
 from sktime.forecasting.trend._stl_forecaster import STLForecaster
 from sktime.forecasting.trend._trend_forecaster import TrendForecaster
diff --git a/sktime/forecasting/trend/_spline_trend_forecaster.py b/sktime/forecasting/trend/_spline_trend_forecaster.py
new file mode 100644
index 000000000..cfc9fb78d
--- /dev/null
+++ b/sktime/forecasting/trend/_spline_trend_forecaster.py
@@ -0,0 +1,183 @@
+# Copyright: sktime developers, BSD-3-Clause License (see LICENSE file)
+
+"""SplineTrendForecaster implementation."""
+
+__author__ = ["jgyasu", "Dehelaan"]
+__all__ = ["SplineTrendForecaster"]
+
+from sklearn.base import clone
+from sklearn.linear_model import LinearRegression
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import SplineTransformer
+
+from sktime.forecasting.base._delegate import _DelegatedForecaster
+
+
+class SplineTrendForecaster(_DelegatedForecaster):
+    r"""Forecast time series data with a spline trend.
+
+    Uses an ``sklearn`` regressor specified by the ``regressor`` parameter
+    to perform regression on time series values against their corresponding indices,
+    after transformation of the indices with ``SplineTransformer``.
+
+    Parameters
+    ----------
+    regressor : sklearn regressor estimator object, default=None
+        Define the regression model type. If not set, defaults to
+        ``sklearn.linear_model.LinearRegression``.
+
+    n_knots : int, default=5
+        Number of knots of the splines if ``knots`` is one of {'uniform', 'quantile'}.
+        Must be at least 2. Ignored if ``knots`` is array-like.
+
+    degree : int, default=1
+        Degree of the splines (1 for linear, 2 for quadratic, etc.).
+    n_knots : int, default=4
+        Number of knots for the spline transformation.
+
+    knots : {'uniform', 'quantile'}or array-like of shape (n_knots, n_features),
+        default='uniform'
+        Determines knot positions such that first knot <= features <= last knot.
+
+        - 'uniform': `n_knots` are distributed uniformly between the
+        min and max values of the features.
+        - 'quantile': `n_knots` are distributed uniformly along the quantiles
+        of the features.
+        - array-like: Specifies sorted knot positions, including the boundary knots.
+        Internally, additional knots are added before the first knot and after
+        the last knot based on the spline degree.
+
+    extrapolation : {'error', 'constant', 'linear', 'continue', 'periodic'},
+        default='constant'
+        Determines how to handle values outside the min and max values of the
+        training features:
+
+        - 'error': Raises a ValueError.
+        - 'constant': Uses the spline value at the minimum or maximum feature as
+        constant extrapolation.
+        - 'linear': Applies linear extrapolation.
+        - 'continue': Extrapolates as is (equivalent to `extrapolate=True` in
+        `scipy.interpolate.BSpline`).
+        - 'periodic': Uses periodic splines with a periodicity equal to the distance
+        between the first and last knot, enforcing equal function values and
+        derivatives at these knots.
+
+    with_intercept : bool, default=True
+        If True, includes a feature in which all polynomial powers are
+        zero (i.e., a column of ones, acting as an intercept term in a linear
+        model).
+
+    Examples
+    --------
+    >>> from sktime.datasets import load_airline
+    >>> from sktime.forecasting.trend import SplineTrendForecaster
+    >>> y = load_airline()
+    >>> forecaster = SplineTrendForecaster(
+    ...     n_knots=5,
+    ...     degree=2,
+    ...     knots="uniform",
+    ...     extrapolation="constant"
+    ... )
+    >>> forecaster.fit(y)
+    SplineTrendForecaster(...)
+    >>> y_pred = forecaster.predict(fh=[1, 2, 3])
+    """
+
+    _delegate_name = "forecaster_"
+
+    _tags = {
+        "authors": ["jgyasu", "Dehelaan"],
+        "maintainers": ["jgyasu"],
+        "ignores-exogeneous-X": True,
+        "requires-fh-in-fit": False,
+        "handles-missing-data": False,
+        "capability:pred_int": False,
+    }
+
+    def __init__(
+        self,
+        regressor=None,
+        n_knots=5,
+        degree=1,
+        knots="uniform",
+        extrapolation="constant",
+        with_intercept=True,
+    ):
+        self.regressor = regressor
+        self.degree = degree
+        self.n_knots = n_knots
+        self.knots = knots
+        self.extrapolation = extrapolation
+        self.with_intercept = with_intercept
+
+        super().__init__()
+
+        if self.regressor is None:
+            regressor = LinearRegression(fit_intercept=False)
+        else:
+            regressor = clone(self.regressor)
+
+        spline_regressor = make_pipeline(
+            SplineTransformer(
+                n_knots=self.n_knots,
+                degree=self.degree,
+                knots=self.knots,
+                extrapolation=self.extrapolation,
+                include_bias=self.with_intercept,
+            ),
+            regressor,
+        )
+
+        from sktime.forecasting.trend import TrendForecaster
+
+        self.forecaster_ = TrendForecaster(spline_regressor)
+
+    @classmethod
+    def get_test_params(cls, parameter_set="default"):
+        """Return testing parameter settings for the estimator.
+
+        Parameters
+        ----------
+        parameter_set : str, default="default"
+            Name of the set of test parameters to return, for use in tests. If no
+            special parameters are defined for a value, will return ``"default"`` set.
+
+        Returns
+        -------
+        params : dict or list of dict, default = {}
+            Parameters to create testing instances of the class
+            Each dict are parameters to construct an "interesting" test instance, i.e.,
+            ``MyClass(**params)`` or ``MyClass(**params[i])`` creates a valid test
+            instance.
+            ``create_test_instance`` uses the first (or only) dictionary in ``params``
+        """
+        from sklearn.ensemble import RandomForestRegressor
+
+        params_list = [
+            {},
+            {
+                "n_knots": 5,
+                "degree": 1,
+                "knots": "uniform",
+                "extrapolation": "constant",
+                "with_intercept": False,
+            },
+            {
+                "regressor": RandomForestRegressor(),
+                "n_knots": 4,
+                "degree": 1,
+                "knots": "quantile",
+                "extrapolation": "linear",
+                "with_intercept": True,
+            },
+            {
+                "regressor": RandomForestRegressor(),
+                "n_knots": 3,
+                "degree": 2,
+                "knots": "uniform",
+                "extrapolation": "periodic",
+                "with_intercept": False,
+            },
+        ]
+
+        return params_list
diff --git a/sktime/networks/cnn.py b/sktime/networks/cnn.py
index 663b16c30..b4dce3e99 100644
--- a/sktime/networks/cnn.py
+++ b/sktime/networks/cnn.py
@@ -9,7 +9,7 @@ from sktime.utils.dependencies import _check_dl_dependencies
 class CNNNetwork(BaseDeepNetwork):
     """Establish the network structure for a CNN.
 
-    Adapted from the implementation used in [1]
+    Adapted from the implementation used in [1]_.
 
     Parameters
     ----------
@@ -31,18 +31,18 @@ class CNNNetwork(BaseDeepNetwork):
     random_state    : int, default = 0
         seed to any needed random actions
 
-    Notes
-    -----
-    Adapted from source code
-    https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/cnn.py
-
     References
     ----------
     .. [1] Zhao et al. Convolutional neural networks for time series classification,
     Journal of Systems Engineering and Electronics 28(1), 162--169, 2017
     """
 
-    _tags = {"python_dependencies": "tensorflow"}
+    _tags = {
+        # packaging info
+        # --------------
+        "authors": ["hfawaz"],
+        "python_dependencies": "tensorflow",
+    }
 
     def __init__(
         self,
diff --git a/sktime/networks/fcn.py b/sktime/networks/fcn.py
index 9626ed965..185fe27a4 100644
--- a/sktime/networks/fcn.py
+++ b/sktime/networks/fcn.py
@@ -9,18 +9,16 @@ from sktime.utils.dependencies import _check_dl_dependencies
 class FCNNetwork(BaseDeepNetwork):
     """Establish the network structure for a FCN.
 
-    Adapted from the implementation used in [1]
+    Adapted from the implementation of Fawaz et. al
+    https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/fcn.py
+
+    Implements network in [1]_.
 
     Parameters
     ----------
-    random_state    : int, default = 0
+    random_states : int, default = 0
         seed to any needed random actions
 
-    Notes
-    -----
-    Adapted from the implementation from Fawaz et. al
-    https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/fcn.py
-
     References
     ----------
     .. [1] Network originally defined in:
diff --git a/sktime/networks/inceptiontime.py b/sktime/networks/inceptiontime.py
index 379111e3a..0c2ab1a32 100644
--- a/sktime/networks/inceptiontime.py
+++ b/sktime/networks/inceptiontime.py
@@ -24,7 +24,10 @@ class InceptionTimeNetwork(BaseDeepNetwork):
                     ArXiv}, Year                     = {2019} }
     """
 
-    _tags = {"python_dependencies": "tensorflow"}
+    _tags = {
+        "authors": ["hfawaz", "JamesLarge", "Withington"],
+        "python_dependencies": "tensorflow",
+    }
 
     def __init__(
         self,
diff --git a/sktime/networks/mcdcnn.py b/sktime/networks/mcdcnn.py
index 6d82745b7..cd1e8f351 100644
--- a/sktime/networks/mcdcnn.py
+++ b/sktime/networks/mcdcnn.py
@@ -1,6 +1,6 @@
 """Multi Channel Deep Convolution Neural Network (MCDCNN)."""
 
-__author__ = ["James Large", "Withington"]
+__author__ = ["James-Large", "Withington"]
 
 from sktime.networks.base import BaseDeepNetwork
 from sktime.utils.dependencies import _check_dl_dependencies
@@ -10,7 +10,7 @@ class MCDCNNNetwork(BaseDeepNetwork):
     """
     Multi Channel Deep Convolutional Neural Network (MCDCNN).
 
-    Adapted from the implementation from Fawaz et. al:
+    Adapted from the implementation of Fawaz et. al:
     https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/mcdcnn.py
 
     Parameters
@@ -35,7 +35,10 @@ class MCDCNNNetwork(BaseDeepNetwork):
         The seed to any random action.
     """
 
-    _tags = {"python_dependencies": "tensorflow"}
+    _tags = {
+        "authors": ["hfawaz", "James-Large", "Withington"],
+        "python_dependencies": "tensorflow",
+    }
 
     def __init__(
         self,
diff --git a/sktime/networks/mlp.py b/sktime/networks/mlp.py
index 6c5912650..dfbccbda0 100644
--- a/sktime/networks/mlp.py
+++ b/sktime/networks/mlp.py
@@ -9,18 +9,16 @@ from sktime.utils.dependencies import _check_dl_dependencies
 class MLPNetwork(BaseDeepNetwork):
     """Establish the network structure for a MLP.
 
-    Adapted from the implementation used in [1]
+    Adapted from the implementation from source code
+    https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/mlp.py
+
+    Implements a simple MLP network, as in [1]_.
 
     Parameters
     ----------
     random_state    : int, default = 0
         seed to any needed random actions
 
-    Notes
-    -----
-    Adapted from the implementation from source code
-    https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/mlp.py
-
     References
     ----------
     .. [1]  Network originally defined in:
@@ -31,7 +29,10 @@ class MLPNetwork(BaseDeepNetwork):
     1578--1585}, year={2017}, organization={IEEE} }
     """
 
-    _tags = {"python_dependencies": "tensorflow"}
+    _tags = {
+        "authors": ["hfawaz", "James-Large", "Withington", "AurumnPegasus"],
+        "python_dependencies": "tensorflow",
+    }
 
     def __init__(
         self,
diff --git a/sktime/networks/resnet.py b/sktime/networks/resnet.py
index 0c5630b1a..53c3f7844 100644
--- a/sktime/networks/resnet.py
+++ b/sktime/networks/resnet.py
@@ -1,6 +1,6 @@
 """Residual Network (ResNet) (minus the final output layer)."""
 
-__author__ = ["James Large", "Withington", "nilesh05apr"]
+__author__ = ["James-Large", "Withington", "nilesh05apr"]
 
 from sktime.networks.base import BaseDeepNetwork
 from sktime.utils.dependencies import _check_dl_dependencies
@@ -9,18 +9,14 @@ from sktime.utils.dependencies import _check_dl_dependencies
 class ResNetNetwork(BaseDeepNetwork):
     """Establish the network structure for a ResNet.
 
-    Adapted from the implementations used in [1]
+    Adapted from the implementation in
+    https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/resnet.py
 
     Parameters
     ----------
     random_state : int, optional (default = 0)
         The random seed to use random activities.
 
-    Notes
-    -----
-    Adapted from the implementation source code
-    https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/resnet.py
-
     References
     ----------
     .. [1] H. Fawaz, G. B. Lanckriet, F. Petitjean, and L. Idoumghar,
@@ -34,7 +30,10 @@ class ResNetNetwork(BaseDeepNetwork):
     1578--1585}, year={2017}, organization={IEEE} }
     """
 
-    _tags = {"python_dependencies": ["tensorflow", "keras-self-attention"]}
+    _tags = {
+        "authors": ["hfawaz", "James-Large", "Withington", "nilesh05apr"],
+        "python_dependencies": ["tensorflow", "keras-self-attention"],
+    }
 
     def __init__(self, random_state=0):
         _check_dl_dependencies(severity="error")
diff --git a/sktime/performance_metrics/detection/__init__.py b/sktime/performance_metrics/detection/__init__.py
new file mode 100644
index 000000000..b8e80b14c
--- /dev/null
+++ b/sktime/performance_metrics/detection/__init__.py
@@ -0,0 +1,7 @@
+"""Metrics for detection tasks."""
+
+from sktime.performance_metrics.detection._chamfer import DirectedChamfer
+from sktime.performance_metrics.detection._count import DetectionCount
+from sktime.performance_metrics.detection._hausdorff import DirectedHausdorff
+
+__all__ = ["DirectedChamfer", "DirectedHausdorff", "DetectionCount"]
diff --git a/sktime/performance_metrics/detection/_base.py b/sktime/performance_metrics/detection/_base.py
new file mode 100644
index 000000000..fdc1978ad
--- /dev/null
+++ b/sktime/performance_metrics/detection/_base.py
@@ -0,0 +1,176 @@
+"""Base class for detection metrics."""
+# copyright: sktime developers, BSD-3-Clause License (see LICENSE file)
+
+from sktime.datatypes import check_is_scitype, convert_to
+from sktime.detection._datatypes._check import (
+    _is_points_dtype,
+    _is_segments_dtype,
+)
+from sktime.detection._datatypes._convert import (
+    _convert_points_to_segments,
+    _convert_segments_to_points,
+)
+from sktime.performance_metrics.base import BaseMetric
+
+
+class BaseDetectionMetric(BaseMetric):
+    """Base class for defining detection error metrics in sktime."""
+
+    _tags = {
+        "object_type": ["metric_detection", "metric"],
+        "scitype:y": "points",  # or segments
+        "requires_X": False,
+        "requires_y_true": True,  # if False, is unsupervised metric
+        "lower_is_better": True,
+    }
+
+    def __call__(self, y_true=None, y_pred=None, X=None):
+        """Evaluate the desired metric on given inputs.
+
+        Parameters
+        ----------
+        y_true : time series in ``sktime`` compatible data container format.
+            Ground truth (correct) event locations, in ``X``.
+            Should be ``pd.DataFrame``, ``pd.Series``, or ``np.ndarray`` (1D or 2D),
+            of ``Series`` scitype = individual time series.
+
+            For further details on data format, see glossary on :term:`mtype`.
+
+        y_pred : time series in ``sktime`` compatible data container format
+            Detected events to evaluate against ground truth.
+            Must be of same format as ``y_true``, same indices and columns if indexed.
+            Not required for unsupervised metrics.
+
+        X : optional, pd.DataFrame, pd.Series or np.ndarray
+            Time series that is being labelled.
+            If not provided, assumes ``RangeIndex`` for ``X``, and that
+            values in ``X`` do not matter.
+
+        Returns
+        -------
+        loss : float
+            Calculated metric.
+        """
+        return self.evaluate(y_true, y_pred, X)
+
+    def evaluate(self, y_true=None, y_pred=None, X=None):
+        """Evaluate the desired metric on given inputs.
+
+        Parameters
+        ----------
+        y_true : time series in ``sktime`` compatible data container format.
+            Ground truth (correct) event locations, in ``X``.
+
+            Not required if unsupervised metric,
+            that is, if tag ``requires_y_true`` is False.
+
+            Should be ``pd.DataFrame``, ``pd.Series``, or ``np.ndarray`` (1D or 2D),
+            of ``Series`` scitype = individual time series.
+
+            For further details on data format, see glossary on :term:`mtype`.
+
+        y_pred : time series in ``sktime`` compatible data container format
+            Detected events to evaluate against ground truth.
+            Must be of same format as ``y_true``, same indices and columns if indexed.
+
+        X : optional, pd.DataFrame, pd.Series or np.ndarray
+            Time series that is being labelled.
+            If not provided, assumes ``RangeIndex`` for ``X``, and that
+            values in ``X`` do not matter.
+
+            Required if tag ``requires_X`` is True.
+
+        Returns
+        -------
+        loss : float
+            Calculated metric.
+        """
+        # Input checks and conversions
+        y_true_inner, y_pred_inner, X_inner = self._check_ys(y_true, y_pred, X)
+
+        # pass to inner function
+        out = self._evaluate(y_true=y_true_inner, y_pred=y_pred_inner, X=X_inner)
+
+        if not isinstance(out, float):
+            out = float(out)
+        return out
+
+    def _evaluate(self, y_true, y_pred, X):
+        """Evaluate the desired metric on given inputs.
+
+        private _evaluate containing core logic, called from evaluate
+
+        Parameters
+        ----------
+        y_true : time series in ``sktime`` compatible data container format.
+            Ground truth (correct) event locations, in ``X``.
+            Should be ``pd.DataFrame``, ``pd.Series``, or ``np.ndarray`` (1D or 2D),
+            of ``Series`` scitype = individual time series.
+
+            For further details on data format, see glossary on :term:`mtype`.
+
+        y_pred : time series in ``sktime`` compatible data container format
+            Detected events to evaluate against ground truth.
+            Must be of same format as ``y_true``, same indices and columns if indexed.
+
+        X : optional, pd.DataFrame, pd.Series or np.ndarray
+            Time series that is being labelled.
+            If not provided, assumes ``RangeIndex`` for ``X``, and that
+            values in ``X`` do not matter.
+
+        Returns
+        -------
+        loss : float
+            Calculated metric.
+        """
+        raise NotImplementedError("Abstract method.")
+
+    def _check_ys(self, y_true, y_pred, X):
+        SCITYPES = ["Series"]
+        INNER_MTYPES = ["pd.DataFrame"]
+
+        def _coerce_to_df(y, var_name="y", allow_none=False):
+            if allow_none and y is None:
+                return None
+            valid, msg, _ = check_is_scitype(
+                y, scitype=SCITYPES, return_metadata=[], var_name=var_name
+            )
+            if not valid:
+                raise TypeError(msg)
+            y_inner = convert_to(y, to_type=INNER_MTYPES)
+            return y_inner
+
+        allow_none_y_true = not self.get_tag("requires_y_true")
+        allow_none_X = not self.get_tag("requires_X")
+
+        # catch the case where y_pred is passed as single positional arg
+        if allow_none_y_true and y_pred is None and y_true is not None:
+            y_pred = y_true
+            y_true = None
+
+        y_true = _coerce_to_df(y_true, var_name="y_true", allow_none=allow_none_y_true)
+        y_pred = _coerce_to_df(y_pred, var_name="y_pred")
+        X = _coerce_to_df(X, var_name="X", allow_none=allow_none_X)
+
+        # coerce to detection type
+        y_true = self._coerce_to_detection_type(y_true, X, allow_none=allow_none_y_true)
+        y_pred = self._coerce_to_detection_type(y_pred, X)
+
+        return y_true, y_pred, X
+
+    def _coerce_to_detection_type(self, y, X, allow_none=False):
+        """Coerce input to detection type."""
+        if allow_none and y is None:
+            return None
+
+        detection_type = self.get_tag("scitype:y")
+        if X is None:
+            len_X = None
+        else:
+            len_X = len(X)
+
+        if _is_points_dtype(y) and detection_type == "segments":
+            y = _convert_points_to_segments(y, len_X=len_X)
+        elif _is_segments_dtype(y) and detection_type == "points":
+            y = _convert_segments_to_points(y, len_X=len_X)
+        return y
diff --git a/sktime/performance_metrics/detection/_chamfer.py b/sktime/performance_metrics/detection/_chamfer.py
new file mode 100644
index 000000000..fbca0c849
--- /dev/null
+++ b/sktime/performance_metrics/detection/_chamfer.py
@@ -0,0 +1,121 @@
+"""Directed Chamfer distance between two sets of points."""
+
+import numpy as np
+import pandas as pd
+
+from sktime.performance_metrics.detection._base import BaseDetectionMetric
+from sktime.performance_metrics.detection.utils import _find_closest_elements
+
+
+class DirectedChamfer(BaseDetectionMetric):
+    r"""Directed Chamfer distance between event points.
+
+    For detected time points :math:`A = (a_1, a_2, \ldots, a_n)` and true time points
+    :math:`B = (b_1, b_2, \ldots, b_m)`,
+    the directed (unnormalized) Chamfer distance is defined as:
+
+    .. math::
+
+        d(A, B) = \sum_{i=1}^{n} \left| a_i - b'_i \right|
+
+    where :math:`b'_i` is the true event closest to :math:`a_i`,
+    that is, :math:`b'_i = \arg \min_{b\in B} |a_i - b|`.
+
+    If ``X`` is provided, the time points are taken as the location indices in ``X``.
+    Otherwise, it is assumed that ``X`` has a ``RangeIndex``.
+
+    Parameters
+    ----------
+    normalize : bool, default=False
+        If True, the Chamfer distance is normalized by the number of detected events,
+        i.e., divided by :math:`n`.
+        If unnormalized, making too many detections will be penalized, wherease
+        the normalized distance penalizes only the average distance to the
+        closest true events, regardless of the number of detections.
+    """
+
+    _tags = {
+        "scitype:y": "points",  # or segments
+        "requires_X": False,
+        "lower_is_better": True,
+    }
+
+    def __init__(self, normalize=False):
+        self.normalize = normalize
+
+        super().__init__()
+
+    def _evaluate(self, y_true, y_pred, X=None):
+        """Evaluate the desired metric on given inputs.
+
+        private _evaluate containing core logic, called from evaluate
+
+        Parameters
+        ----------
+        y_true : time series in ``sktime`` compatible data container format.
+            Ground truth (correct) event locations, in ``X``.
+            Should be ``pd.DataFrame``, ``pd.Series``, or ``np.ndarray`` (1D or 2D),
+            of ``Series`` scitype = individual time series.
+
+            For further details on data format, see glossary on :term:`mtype`.
+
+        y_pred : time series in ``sktime`` compatible data container format
+            Detected events to evaluate against ground truth.
+            Must be of same format as ``y_true``, same indices and columns if indexed.
+
+        X : optional, pd.DataFrame, pd.Series or np.ndarray
+            Time series that is being labelled.
+            If not provided, assumes ``RangeIndex`` for ``X``, and that
+            values in ``X`` do not matter.
+
+        Returns
+        -------
+        loss : float
+            Calculated metric.
+        """
+        y_true_ilocs = y_true.ilocs
+        y_pred_ilocs = y_pred.ilocs
+
+        if X is not None and not isinstance(X.index, pd.RangeIndex):
+            y_true_locs = X.index[y_true_ilocs]
+            y_pred_locs = X.index[y_pred_ilocs]
+        else:
+            y_true_locs = y_true_ilocs
+            y_pred_locs = y_pred_ilocs
+
+        y_true_locs = y_true_locs.to_numpy()
+        y_pred_locs = y_pred_locs.to_numpy()
+
+        y_true_closest = _find_closest_elements(y_pred_locs, y_true_locs)
+        y_true_closest = np.array(y_true_closest)
+
+        distance = np.sum(np.abs(y_true_closest - y_pred_locs))
+
+        if self.normalize:
+            distance /= len(y_pred_locs)
+
+        return distance
+
+    @classmethod
+    def get_test_params(cls, parameter_set="default"):
+        """Return testing parameter settings for the estimator.
+
+        Parameters
+        ----------
+        parameter_set : str, default="default"
+            Name of the set of test parameters to return, for use in tests. If no
+            special parameters are defined for a value, will return ``"default"`` set.
+
+        Returns
+        -------
+        params : dict or list of dict, default={}
+            Parameters to create testing instances of the class.
+            Each dict are parameters to construct an "interesting" test instance, i.e.,
+            ``MyClass(**params)`` or ``MyClass(**params[i])`` creates a valid test
+            instance.
+            ``create_test_instance`` uses the first (or only) dictionary in ``params``.
+        """
+        param1 = {}
+        param2 = {"normalize": True}
+
+        return [param1, param2]
diff --git a/sktime/performance_metrics/detection/_count.py b/sktime/performance_metrics/detection/_count.py
new file mode 100644
index 000000000..c29adf0e4
--- /dev/null
+++ b/sktime/performance_metrics/detection/_count.py
@@ -0,0 +1,101 @@
+"""Count of detection, possibly in excess or deviation of a target count."""
+
+from sktime.performance_metrics.detection._base import BaseDetectionMetric
+
+
+class DetectionCount(BaseDetectionMetric):
+    r"""Count of detection, possibly in excess or deviation of a target count.
+
+    Parameters
+    ----------
+    target : int, default=0
+        Target number of detections.
+        If 0, the count is the absolute number of detections.
+        If positive, the count reported is, by default, the absolute
+        difference between the number of detections and the target.
+        If the ``excess_only`` parameter is set to ``True``, the count
+        is the number of detections in excess of the target if larger,
+        otherwise zero.
+    excess_only : bool, default=False
+        If False, the count is the absolute difference between the ``target``
+        and the number of detections.
+        If True, the count is the number of detections in excess of the target
+        if larger, otherwise zero.
+    """
+
+    _tags = {
+        "scitype:y": "points",  # or segments
+        "requires_X": False,
+        "requires_y_true": False,  # this is an unsupervised metric
+        "lower_is_better": True,
+    }
+
+    def __init__(self, target=0, excess_only=False):
+        self.target = target
+        self.excess_only = excess_only
+
+        super().__init__()
+
+    def _evaluate(self, y_true, y_pred, X):
+        """Evaluate the desired metric on given inputs.
+
+        private _evaluate containing core logic, called from evaluate
+
+        Parameters
+        ----------
+        y_true : time series in ``sktime`` compatible data container format.
+            Ground truth (correct) event locations, in ``X``.
+            Should be ``pd.DataFrame``, ``pd.Series``, or ``np.ndarray`` (1D or 2D),
+            of ``Series`` scitype = individual time series.
+
+            For further details on data format, see glossary on :term:`mtype`.
+
+        y_pred : time series in ``sktime`` compatible data container format
+            Detected events to evaluate against ground truth.
+            Must be of same format as ``y_true``, same indices and columns if indexed.
+
+        X : optional, pd.DataFrame, pd.Series or np.ndarray
+            Time series that is being labelled.
+            If not provided, assumes ``RangeIndex`` for ``X``, and that
+            values in ``X`` do not matter.
+
+        Returns
+        -------
+        loss : float
+            Calculated metric.
+        """
+        num_detections = len(y_pred)
+
+        score = num_detections - self.target
+
+        if self.excess_only:
+            score = max(score, 0)
+
+        score = abs(score)
+
+        return score
+
+    @classmethod
+    def get_test_params(cls, parameter_set="default"):
+        """Return testing parameter settings for the estimator.
+
+        Parameters
+        ----------
+        parameter_set : str, default="default"
+            Name of the set of test parameters to return, for use in tests. If no
+            special parameters are defined for a value, will return ``"default"`` set.
+
+        Returns
+        -------
+        params : dict or list of dict, default={}
+            Parameters to create testing instances of the class.
+            Each dict are parameters to construct an "interesting" test instance, i.e.,
+            ``MyClass(**params)`` or ``MyClass(**params[i])`` creates a valid test
+            instance.
+            ``create_test_instance`` uses the first (or only) dictionary in ``params``.
+        """
+        param0 = {}
+        param1 = {"target": 1, "excess_only": True}
+        param2 = {"target": 42}
+
+        return [param0, param1, param2]
diff --git a/sktime/performance_metrics/detection/_hausdorff.py b/sktime/performance_metrics/detection/_hausdorff.py
new file mode 100644
index 000000000..d27acedb9
--- /dev/null
+++ b/sktime/performance_metrics/detection/_hausdorff.py
@@ -0,0 +1,80 @@
+"""Directed Hausdorff distance between two sets of points."""
+
+import numpy as np
+import pandas as pd
+
+from sktime.performance_metrics.detection._base import BaseDetectionMetric
+from sktime.performance_metrics.detection.utils import _find_closest_elements
+
+
+class DirectedHausdorff(BaseDetectionMetric):
+    r"""Directed Hausdorff distance between event points.
+
+    For detected time points :math:`A = (a_1, a_2, \ldots, a_n)` and true time points
+    :math:`B = (b_1, b_2, \ldots, b_m)`,
+    the directed (unnormalized) Hausdorff distance is defined as:
+
+    .. math::
+
+        d(A, B) = \max_i \left| a_i - b'_i \right|
+
+    where :math:`b'_i` is the true event closest to :math:`a_i`,
+    that is, :math:`b'_i = \arg \min_{b\in B} |a_i - b|`.
+
+    If ``X`` is provided, the time points are taken as the location indices in ``X``.
+    Otherwise, it is assumed that ``X`` has a ``RangeIndex``.
+    """
+
+    _tags = {
+        "scitype:y": "points",  # or segments
+        "requires_X": False,
+        "lower_is_better": True,
+    }
+
+    def _evaluate(self, y_true, y_pred, X=None):
+        """Evaluate the desired metric on given inputs.
+
+        private _evaluate containing core logic, called from evaluate
+
+        Parameters
+        ----------
+        y_true : time series in ``sktime`` compatible data container format.
+            Ground truth (correct) event locations, in ``X``.
+            Should be ``pd.DataFrame``, ``pd.Series``, or ``np.ndarray`` (1D or 2D),
+            of ``Series`` scitype = individual time series.
+
+            For further details on data format, see glossary on :term:`mtype`.
+
+        y_pred : time series in ``sktime`` compatible data container format
+            Detected events to evaluate against ground truth.
+            Must be of same format as ``y_true``, same indices and columns if indexed.
+
+        X : optional, pd.DataFrame, pd.Series or np.ndarray
+            Time series that is being labelled.
+            If not provided, assumes ``RangeIndex`` for ``X``, and that
+            values in ``X`` do not matter.
+
+        Returns
+        -------
+        loss : float
+            Calculated metric.
+        """
+        y_true_ilocs = y_true.ilocs
+        y_pred_ilocs = y_pred.ilocs
+
+        if X is not None and not isinstance(X.index, pd.RangeIndex):
+            y_true_locs = X.index[y_true_ilocs]
+            y_pred_locs = X.index[y_pred_ilocs]
+        else:
+            y_true_locs = y_true_ilocs
+            y_pred_locs = y_pred_ilocs
+
+        y_true_locs = y_true_locs.to_numpy()
+        y_pred_locs = y_pred_locs.to_numpy()
+
+        y_true_closest = _find_closest_elements(y_pred_locs, y_true_locs)
+        y_true_closest = np.array(y_true_closest)
+
+        distance = np.max(np.abs(y_true_closest - y_pred_locs))
+
+        return distance
diff --git a/sktime/performance_metrics/detection/utils/__init__.py b/sktime/performance_metrics/detection/utils/__init__.py
new file mode 100644
index 000000000..a588073fb
--- /dev/null
+++ b/sktime/performance_metrics/detection/utils/__init__.py
@@ -0,0 +1,5 @@
+"""Utilities for detection metrics."""
+
+from sktime.performance_metrics.detection.utils._closest import _find_closest_elements
+
+__all__ = ["_find_closest_elements"]
diff --git a/sktime/performance_metrics/detection/utils/_closest.py b/sktime/performance_metrics/detection/utils/_closest.py
new file mode 100644
index 000000000..040f0ce30
--- /dev/null
+++ b/sktime/performance_metrics/detection/utils/_closest.py
@@ -0,0 +1,44 @@
+"""Utility to find all closest elements in sorted list b to sorted list a."""
+
+
+def _find_closest_elements(a, b):
+    """Find the closest element in b for each element in a.
+
+    Parameters
+    ----------
+    a : 1D array-like
+        An ordered (sorted) list of elements.
+    b : 1D array-like
+        Another ordered (sorted) list of elements.
+
+    Returns
+    -------
+    closest : list, same length as a
+        a list of closest elements in ``b`` for each element in ``a``.
+        In case of ties, the first closest element is chosen.
+
+    Examples
+    --------
+    >>> a = [1, 3, 5]
+    >>> b = [2, 3.1, 3.2, 4, 6]
+    >>> pointer = DirectedHausdorff()._find_closest_elements(a, b)
+    """
+    # Pointers for traversing A and B
+    i, j = 0, 0
+    n, m = len(a), len(b)
+
+    # List to store the result
+    result = []
+
+    while i < n:
+        # Move pointer j in b to get the closest value to a[i]
+        while j + 1 < m and abs(b[j + 1] - a[i]) < abs(b[j] - a[i]):
+            j += 1
+
+        # Append the closest value in b for a[i]
+        result.append(b[j])
+
+        # Move to the next element in a
+        i += 1
+
+    return result
diff --git a/sktime/performance_metrics/forecasting/_classes.py b/sktime/performance_metrics/forecasting/_classes.py
index 18866c221..955295b47 100644
--- a/sktime/performance_metrics/forecasting/_classes.py
+++ b/sktime/performance_metrics/forecasting/_classes.py
@@ -711,7 +711,7 @@ class BaseForecastingErrorMetric(BaseMetric):
                 return y.X_multiindex
 
             valid, msg, metadata = check_is_scitype(
-                y, scitype=SCITYPES, return_metadata=True, var_name=var_name
+                y, scitype=SCITYPES, return_metadata=[], var_name=var_name
             )
             if not valid:
                 raise TypeError(msg)
diff --git a/sktime/registry/_base_classes.py b/sktime/registry/_base_classes.py
index 9a54e7e12..269dce5f4 100644
--- a/sktime/registry/_base_classes.py
+++ b/sktime/registry/_base_classes.py
@@ -69,6 +69,11 @@ class _BaseScitypeOfObject(BaseObject):
         "mixin": False,  # whether this is a mixin, not full scitype
     }
 
+    @classmethod
+    def get_test_class(cls):
+        """Return test class for the scitype."""
+        return None
+
 
 class object(_BaseScitypeOfObject):
     """Universal type for all objects."""
@@ -84,6 +89,12 @@ class object(_BaseScitypeOfObject):
 
         return BaseObject
 
+    @classmethod
+    def get_test_class(cls):
+        from sktime.tests.test_all_estimators import TestAllObjects
+
+        return TestAllObjects
+
 
 class estimator(_BaseScitypeOfObject):
     """Estimator objects, i.e., objects with fit method."""
@@ -100,6 +111,12 @@ class estimator(_BaseScitypeOfObject):
 
         return BaseEstimator
 
+    @classmethod
+    def get_test_class(cls):
+        from sktime.tests.test_all_estimators import TestAllEstimators
+
+        return TestAllEstimators
+
 
 class aligner(_BaseScitypeOfObject):
     """Time series aligner or sequence aligner."""
@@ -116,6 +133,12 @@ class aligner(_BaseScitypeOfObject):
 
         return BaseAligner
 
+    @classmethod
+    def get_test_class(cls):
+        from sktime.alignment.tests.test_all_aligners import TestAllAligners
+
+        return TestAllAligners
+
 
 class classifier(_BaseScitypeOfObject):
     """Time series classifier."""
@@ -132,6 +155,12 @@ class classifier(_BaseScitypeOfObject):
 
         return BaseClassifier
 
+    @classmethod
+    def get_test_class(cls):
+        from sktime.classification.tests.test_all_classifiers import TestAllClassifiers
+
+        return TestAllClassifiers
+
 
 class clusterer(_BaseScitypeOfObject):
     """Time series clusterer."""
@@ -164,6 +193,14 @@ class early_classifier(_BaseScitypeOfObject):
 
         return BaseEarlyClassifier
 
+    @classmethod
+    def get_test_class(cls):
+        from sktime.classification.early_classification.tests.test_all_early_classifiers import (  # noqa E501
+            TestAllEarlyClassifiers,  # noqa E501
+        )  # noqa E501
+
+        return TestAllEarlyClassifiers
+
 
 class forecaster(_BaseScitypeOfObject):
     """Time series forecaster."""
@@ -180,6 +217,12 @@ class forecaster(_BaseScitypeOfObject):
 
         return BaseForecaster
 
+    @classmethod
+    def get_test_class(cls):
+        from sktime.forecasting.tests.test_all_forecasters import TestAllForecasters
+
+        return TestAllForecasters
+
 
 class global_forecaster(_BaseScitypeOfObject):
     """Global time series forecaster."""
@@ -196,6 +239,14 @@ class global_forecaster(_BaseScitypeOfObject):
 
         return _BaseGlobalForecaster
 
+    @classmethod
+    def get_test_class(cls):
+        from sktime.forecasting.tests.test_all_forecasters import (
+            TestAllGlobalForecasters,
+        )
+
+        return TestAllGlobalForecasters
+
 
 class metric(_BaseScitypeOfObject):
     """Performance metric for time series."""
@@ -213,6 +264,30 @@ class metric(_BaseScitypeOfObject):
         return BaseMetric
 
 
+class metric_detection(_BaseScitypeOfObject):
+    """Performance metric for time series detection tasks."""
+
+    _tags = {
+        "scitype_name": "metric_detection",
+        "short_descr": "performance metric for detectors",
+        "parent_scitype": "metric",
+    }
+
+    @classmethod
+    def get_base_class(cls):
+        from sktime.performance_metrics.detection._base import BaseDetectionMetric
+
+        return BaseDetectionMetric
+
+    @classmethod
+    def get_test_class(cls):
+        from sktime.performance_metrics.detection.tests.test_all_metrics_detection import (  # noqa E501
+            TestAllDetectionMetrics,  # noqa E501
+        )  # noqa E501
+
+        return TestAllDetectionMetrics
+
+
 class metric_forecasting(_BaseScitypeOfObject):
     """Performance metric for time series forecasting."""
 
@@ -262,6 +337,12 @@ class param_est(_BaseScitypeOfObject):
 
         return BaseParamFitter
 
+    @classmethod
+    def get_test_class(cls):
+        from sktime.param_est.tests.test_all_param_est import TestAllParamFitters
+
+        return TestAllParamFitters
+
 
 class regressor(_BaseScitypeOfObject):
     """Time series regressor."""
@@ -278,6 +359,12 @@ class regressor(_BaseScitypeOfObject):
 
         return BaseRegressor
 
+    @classmethod
+    def get_test_class(cls):
+        from sktime.regression.tests.test_all_regressors import TestAllRegressors
+
+        return TestAllRegressors
+
 
 class detector(_BaseScitypeOfObject):
     """Detector of anomalies, outliers, or change points."""
@@ -294,6 +381,12 @@ class detector(_BaseScitypeOfObject):
 
         return BaseDetector
 
+    @classmethod
+    def get_test_class(cls):
+        from sktime.detection.tests.test_all_detectors import TestAllDetectors
+
+        return TestAllDetectors
+
 
 class splitter(_BaseScitypeOfObject):
     """Time series splitter."""
@@ -310,6 +403,12 @@ class splitter(_BaseScitypeOfObject):
 
         return BaseSplitter
 
+    @classmethod
+    def get_test_class(cls):
+        from sktime.split.tests.test_all_splitters import TestAllSplitters
+
+        return TestAllSplitters
+
 
 class transformer(_BaseScitypeOfObject):
     """Time series transformer."""
@@ -326,6 +425,14 @@ class transformer(_BaseScitypeOfObject):
 
         return BaseTransformer
 
+    @classmethod
+    def get_test_class(cls):
+        from sktime.transformations.tests.test_all_transformers import (
+            TestAllTransformers,
+        )
+
+        return TestAllTransformers
+
 
 class transformer_pairwise(_BaseScitypeOfObject):
     """Pairwise transformer for tabular data, distance or kernel."""
@@ -342,6 +449,14 @@ class transformer_pairwise(_BaseScitypeOfObject):
 
         return BasePairwiseTransformer
 
+    @classmethod
+    def get_test_class(cls):
+        from sktime.dists_kernels.tests.test_all_dist_kernels import (
+            TestAllPairwiseTransformers,
+        )
+
+        return TestAllPairwiseTransformers
+
 
 class transformer_pairwise_panel(_BaseScitypeOfObject):
     """Pairwise transformer for panel data, distance or kernel."""
@@ -358,6 +473,14 @@ class transformer_pairwise_panel(_BaseScitypeOfObject):
 
         return BasePairwiseTransformerPanel
 
+    @classmethod
+    def get_test_class(cls):
+        from sktime.dists_kernels.tests.test_all_dist_kernels import (
+            TestAllPanelTransformers,
+        )
+
+        return TestAllPanelTransformers
+
 
 class distribution(_BaseScitypeOfObject):
     """Pandas-like probability distribution."""
@@ -580,6 +703,12 @@ class series_annotator(_BaseScitypeOfObject):
 
         return BaseDetector
 
+    @classmethod
+    def get_test_class(cls):
+        from sktime.detection.tests.test_all_detectors import TestAllDetectors
+
+        return TestAllDetectors
+
 
 class transformer_series_to_primitives(_BaseScitypeOfObject):
     """LEGACY - time series to primitives transformer."""
diff --git a/sktime/registry/_tags.py b/sktime/registry/_tags.py
index ff8f152e9..0c46bb5b7 100644
--- a/sktime/registry/_tags.py
+++ b/sktime/registry/_tags.py
@@ -1811,6 +1811,44 @@ class y_inner_mtype(_BaseTag):
     }
 
 
+class visual_block_kind(_BaseTag):
+    """How to display html representation of a meta-estimator in a jupyter notebook.
+
+    - String name: ``"visual_block_kind"``
+    - Extension developer tag
+    - Values: string, one of ``"single"``, ``"serial"``, ``"parallel"``
+    - Example: ``"single"``
+    - Default: ``"single"``
+
+    This tag specifies how to display the html representation of a meta-estimator
+    in a jupyter notebook.
+
+    Meta-estimators are composites with a variable number of sub-estimators,
+    such as ``ForecastingPipeline`` or ``ColumnTransformer``, inheriting from
+    ``_HeterogenousMetaEstimator``.
+
+    The html display is triggered by calling the ``_repr_html_`` method on any
+    ``scikit-base`` estimator, which returns a html representation of the estimator,
+    used by default in jupyter notebooks, or also other html display environments.
+
+    Possible values are:
+
+    * ``"single"``: the meta-estimator is displayed as a single block in the notebook.
+    * ``"serial"``: the meta-estimator is displayed as a series of blocks, one for
+      each sub-estimator, in a serial layout, i.e., as a vertical stack.
+    * ``"parallel"``: the meta-estimator is displayed as a series of blocks, one for
+      each sub-estimator, in a parallel layout, i.e., as a horizontal stack.
+    """
+
+    _tags = {
+        "tag_name": "visual_block_kind",
+        "parent_type": "estimator",
+        "tag_type": ("str", ["single", "serial", "parallel"]),
+        "short_descr": "how to display html represantation of a meta-estimator in jupyter notebook",  # noqa: E501
+        "user_facing": False,
+    }
+
+
 ESTIMATOR_TAG_REGISTER = [
     (
         "sktime_version",
@@ -1932,6 +1970,12 @@ ESTIMATOR_TAG_REGISTER = [
         "bool",
         "does metric require a predictive benchmark?",
     ),
+    (
+        "requires_y_true",
+        "metric",
+        "bool",
+        "does metric require ground truth? If False, unsupervised metric",
+    ),
     (
         "scitype:y_pred",
         "metric",
@@ -2111,12 +2155,6 @@ ESTIMATOR_TAG_REGISTER = [
         "dict",
         "deprecated tag for dependency import aliases",
     ),
-    (
-        "visual_block_kind",
-        "estimator",
-        ("str", ["single", "serial", "parallel"]),
-        "how to display html represantation of a meta-estimator in jupyter notebook",
-    ),
 ]
 
 # construct the tag register from all classes in this module
diff --git a/sktime/regression/deep_learning/cnn.py b/sktime/regression/deep_learning/cnn.py
index de9a0a8f6..a0037ad02 100644
--- a/sktime/regression/deep_learning/cnn.py
+++ b/sktime/regression/deep_learning/cnn.py
@@ -15,6 +15,9 @@ from sktime.utils.dependencies import _check_dl_dependencies
 class CNNRegressor(BaseDeepRegressor):
     """Time Series Convolutional Neural Network (CNN), as described in [1].
 
+    Adapted from the implementation from Fawaz et. al
+    https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/cnn.py
+
     Parameters
     ----------
     n_epochs       : int, default = 2000
@@ -77,7 +80,8 @@ class CNNRegressor(BaseDeepRegressor):
     _tags = {
         # packaging info
         # --------------
-        "authors": ["AurumnPegasus", "achieveordie"],
+        "authors": ["hfawaz", "AurumnPegasus", "achieveordie"],
+        # hfawaz for dl-4-tsc
         "maintainers": ["AurumnPegasus", "achieveordie"],
         "python_dependencies": "tensorflow",
         # estimator type handled by parent class
diff --git a/sktime/regression/deep_learning/cntc.py b/sktime/regression/deep_learning/cntc.py
index 830c9dfa6..03d51830f 100644
--- a/sktime/regression/deep_learning/cntc.py
+++ b/sktime/regression/deep_learning/cntc.py
@@ -12,6 +12,9 @@ from sktime.utils.dependencies import _check_dl_dependencies
 class CNTCRegressor(BaseDeepRegressor):
     """Contextual Time-series Neural Regressor (CNTC), as described in [1].
 
+    Adapted from the implementation from Fullah et. al
+    https://github.com/AmaduFullah/CNTC_MODEL/blob/master/cntc.ipynb
+
     Parameters
     ----------
     n_epochs       : int, default = 2000
@@ -38,11 +41,6 @@ class CNTCRegressor(BaseDeepRegressor):
     optimizer       : keras.optimizer, default=keras.optimizers.Adam(),
     metrics         : list of strings, default=["accuracy"],
 
-    Notes
-    -----
-    Adapted from the implementation from Fullah et. al
-    https://github.com/AmaduFullah/CNTC_MODEL/blob/master/cntc.ipynb
-
     References
     ----------
     .. [1] Network originally defined in:
@@ -63,7 +61,12 @@ class CNTCRegressor(BaseDeepRegressor):
     """
 
     _tags = {
-        "authors": ["James-Large", "Withington", "TonyBagnall", "AurumnPegasus"],
+        "authors": [
+            "AmaduFullah",
+            "James-Large",
+            "Withington",
+            "AurumnPegasus",
+        ],
         "maintainers": ["James-Large", "Withington", "AurumnPegasus", "nilesh05apr"],
         "python_dependencies": ["tensorflow", "keras-self-attention"],
     }
diff --git a/sktime/regression/deep_learning/fcn.py b/sktime/regression/deep_learning/fcn.py
index 244de161b..ed3d81dcb 100644
--- a/sktime/regression/deep_learning/fcn.py
+++ b/sktime/regression/deep_learning/fcn.py
@@ -15,6 +15,9 @@ from sktime.utils.dependencies import _check_dl_dependencies
 class FCNRegressor(BaseDeepRegressor):
     """Fully Connected Neural Network (FCN), as described in [1]_.
 
+    Adapted from the implementation from Fawaz et. al
+    https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/fcn.py
+
     Parameters
     ----------
     should inherited fields be listed here?
@@ -38,11 +41,6 @@ class FCNRegressor(BaseDeepRegressor):
     optimizer       : keras.optimizers object, default = Adam(lr=0.01)
         specify the optimizer and the learning rate to be used.
 
-    Notes
-    -----
-    Adapted from the implementation from Fawaz et. al
-    https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/fcn.py
-
     References
     ----------
     .. [1] Zhao et. al, Convolutional neural networks for time series classification,
@@ -52,7 +50,7 @@ class FCNRegressor(BaseDeepRegressor):
     _tags = {
         # packaging info
         # --------------
-        "authors": ["James-Large", "AurumnPegasus"],
+        "authors": ["hfawaz", "James-Large", "AurumnPegasus"],
         "maintainers": ["James-Large", "AurumnPegasus", "nilesh05apr"],
         # estimator type handled by parent class
     }
diff --git a/sktime/regression/deep_learning/inceptiontime.py b/sktime/regression/deep_learning/inceptiontime.py
index f2b704125..6cd95fce7 100644
--- a/sktime/regression/deep_learning/inceptiontime.py
+++ b/sktime/regression/deep_learning/inceptiontime.py
@@ -15,6 +15,9 @@ from sktime.utils.dependencies import _check_dl_dependencies
 class InceptionTimeRegressor(BaseDeepRegressor):
     """InceptionTime Deep Learning Regressor.
 
+    Adapted from the implementation of Fawaz et. al
+    https://github.com/hfawaz/InceptionTime/blob/master/classifiers/inception.py
+
     Parameters
     ----------
     n_epochs : int, default=1500
@@ -39,15 +42,12 @@ class InceptionTimeRegressor(BaseDeepRegressor):
     -----
     ..[1] Fawaz et. al, InceptionTime: Finding AlexNet for Time Series
     Classification, Data Mining and Knowledge Discovery, 34, 2020
-
-    Adapted from the implementation from Fawaz et. al
-    https://github.com/hfawaz/InceptionTime/blob/master/classifiers/inception.py
     """
 
     _tags = {
         # packaging info
         # --------------
-        "authors": ["james-large"],
+        "authors": ["hfawaz", "james-large"],
         "maintainers": ["james-large", "niles05apr"],
         # estimator type handled by parent class
     }
diff --git a/sktime/regression/deep_learning/mcdcnn.py b/sktime/regression/deep_learning/mcdcnn.py
index 01ea12725..3b235d718 100644
--- a/sktime/regression/deep_learning/mcdcnn.py
+++ b/sktime/regression/deep_learning/mcdcnn.py
@@ -15,6 +15,9 @@ from sktime.utils.dependencies import _check_dl_dependencies
 class MCDCNNRegressor(BaseDeepRegressor):
     """Multi Channel Deep Convolutional Neural Regressor, adopted from [1]_.
 
+    Adapted from the implementation of Fawaz et. al
+    https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/mcdcnn.py
+
     Parameters
     ----------
     n_epochs : int, optional (default=120)
@@ -56,11 +59,6 @@ class MCDCNNRegressor(BaseDeepRegressor):
     random_state : int, optional (default=0)
         The seed to any random action.
 
-    Notes
-    -----
-    Adapted from the implementation of Fawaz et. al
-    https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/mcdcnn.py
-
     References
     ----------
     .. [1] Zheng et. al, Time series classification using multi-channels deep
@@ -80,7 +78,7 @@ class MCDCNNRegressor(BaseDeepRegressor):
     _tags = {
         # packaging info
         # --------------
-        "authors": ["James-Large"],
+        "authors": ["hfawaz", "James-Large"],
         "python_dependencies": "tensorflow",
         # estimator type handled by parent class
     }
diff --git a/sktime/regression/deep_learning/mlp.py b/sktime/regression/deep_learning/mlp.py
index ead20dbd3..ea752b81c 100644
--- a/sktime/regression/deep_learning/mlp.py
+++ b/sktime/regression/deep_learning/mlp.py
@@ -15,6 +15,9 @@ from sktime.utils.dependencies import _check_dl_dependencies
 class MLPRegressor(BaseDeepRegressor):
     """Multi Layer Perceptron Network (MLP), as described in [1]_.
 
+    Adapted from the implementation by hfawaz in
+    https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/mlp.py
+
     Parameters
     ----------
     should inherited fields be listed here?
@@ -38,11 +41,6 @@ class MLPRegressor(BaseDeepRegressor):
     optimizer       : keras.optimizers object, default = Adam(lr=0.01)
         specify the optimizer and the learning rate to be used.
 
-    Notes
-    -----
-    Adapted from the implementation from source code
-    https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/mlp.py
-
     References
     ----------
     .. [1] Wang et. al, Time series classification from
@@ -64,7 +62,7 @@ class MLPRegressor(BaseDeepRegressor):
     _tags = {
         # packaging info
         # --------------
-        "authors": ["James-Large", "AurumnPegasus"],
+        "authors": ["hfawaz", "James-Large", "AurumnPegasus"],
         "maintainers": ["James-Large", "AurumnPegasus", "nilesh05apr"],
         # estimator type handled by parent class
     }
diff --git a/sktime/regression/deep_learning/resnet.py b/sktime/regression/deep_learning/resnet.py
index 2a8ce52aa..557b2dc49 100644
--- a/sktime/regression/deep_learning/resnet.py
+++ b/sktime/regression/deep_learning/resnet.py
@@ -14,6 +14,9 @@ from sktime.utils.dependencies import _check_dl_dependencies
 class ResNetRegressor(BaseDeepRegressor):
     """Residual Neural Network Regressor adopted from [1].
 
+    Adapted from the implementation by hfawaz in
+    https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/resnet.py
+
     Parameters
     ----------
     n_epochs       : int, default = 1500
@@ -37,12 +40,6 @@ class ResNetRegressor(BaseDeepRegressor):
     optimizer       : keras.optimizers object, default = Adam(lr=0.01)
         specify the optimizer and the learning rate to be used.
 
-
-    Notes
-    -----
-    Adapted from the implementation from source code
-    https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/resnet.py
-
     References
     ----------
         .. [1] Wang et. al, Time series classification from
@@ -62,7 +59,8 @@ class ResNetRegressor(BaseDeepRegressor):
     _tags = {
         # packaging info
         # --------------
-        "authors": ["James-Large", "Withington"],
+        "authors": ["hfawaz", "James-Large", "Withington"],
+        # hfawaz for dl-4-tsc
         "maintainers": ["Withington"],
         "python_dependencies": "tensorflow",
         # estimator type handled by parent class
diff --git a/sktime/split/instance.py b/sktime/split/instance.py
index c36b72c80..678163538 100644
--- a/sktime/split/instance.py
+++ b/sktime/split/instance.py
@@ -2,7 +2,7 @@
 # copyright: sktime developers, BSD-3-Clause License (see LICENSE file)
 """Splitter that uses an sklearn splitter to split by instance."""
 
-__author__ = ["fkiraly"]
+__author__ = ["fkiraly", "ksharma6"]
 
 __all__ = [
     "InstanceSplitter",
@@ -10,11 +10,11 @@ __all__ = [
 
 from typing import Optional
 
-import numpy as np
 import pandas as pd
 
 from sktime.split.base import BaseSplitter
 from sktime.split.base._common import ACCEPTED_Y_TYPES, SPLIT_GENERATOR_TYPE
+from sktime.utils.multiindex import apply_split
 
 
 class InstanceSplitter(BaseSplitter):
@@ -62,15 +62,10 @@ class InstanceSplitter(BaseSplitter):
             y = pd.MultiIndex.from_arrays([zeros, y])
 
         inst_ix = y.droplevel(-1).unique()
-        iloc_ixer = pd.DataFrame(pd.RangeIndex(len(y)), index=y)
 
         for y_train_inst_iloc, y_test_inst_iloc in self.cv.split(inst_ix):
-            y_train_inst_loc = inst_ix[np.array(y_train_inst_iloc)]
-            y_test_inst_loc = inst_ix[np.array(y_test_inst_iloc)]
-            tr_np = [iloc_ixer.loc[x].to_numpy().flatten() for x in y_train_inst_loc]
-            tt_np = [iloc_ixer.loc[x].to_numpy().flatten() for x in y_test_inst_loc]
-            y_train_iloc = np.concatenate(tr_np)
-            y_test_iloc = np.concatenate(tt_np)
+            y_train_iloc = apply_split(y, y_train_inst_iloc)
+            y_test_iloc = apply_split(y, y_test_inst_iloc)
             yield y_train_iloc, y_test_iloc
 
     def get_n_splits(self, y: Optional[ACCEPTED_Y_TYPES] = None) -> int:
diff --git a/sktime/utils/dependencies/_dependencies.py b/sktime/utils/dependencies/_dependencies.py
index 1c4a1322b..34e5b84bc 100644
--- a/sktime/utils/dependencies/_dependencies.py
+++ b/sktime/utils/dependencies/_dependencies.py
@@ -1,7 +1,5 @@
 """Utility to check soft dependency imports, and raise warnings or errors."""
 
-__author__ = ["fkiraly", "mloning"]
-
 import sys
 import warnings
 from functools import lru_cache
@@ -131,23 +129,20 @@ def _check_soft_dependencies(
         if pkg_env_version is None:
             if obj is None and msg is None:
                 msg = (
-                    f"{package!r} not found. "
-                    f"{package!r} is a soft dependency and not included in the "
-                    f"base sktime installation. Please run: `pip install {package}` to "
-                    f"install the {package} package. "
-                    f"To install all soft dependencies, run: `pip install "
-                    f"sktime[all_extras]`"
+                    f"{class_name} requires package {package!r} to be present "
+                    f"in the python environment, but {package!r} was not found. "
                 )
             elif msg is None:  # obj is not None, msg is None
                 msg = (
                     f"{class_name} requires package {package!r} to be present "
                     f"in the python environment, but {package!r} was not found. "
-                    f"{package!r} is a soft dependency and not included in the base "
-                    f"sktime installation. Please run: `pip install {package}` to "
-                    f"install the {package} package. "
-                    f"To install all soft dependencies, run: `pip install "
-                    f"sktime[all_extras]`"
+                    f"{package!r} is a dependency of {class_name} and required "
+                    f"to construct it. "
                 )
+            msg = msg + (
+                f"To install the requirement {package!r}, please run: "
+                f"pip install {package}` "
+            )
             # if msg is not None, none of the above is executed,
             # so if msg is passed it overrides the default messages
 
@@ -221,10 +216,10 @@ def _check_mlflow_dependencies(msg=None, severity="error"):
     Parameters
     ----------
     msg: str, optional, default= default message (msg below)
-        error message to be returned when `ModuleNotFoundError` is raised.
+        error message to be returned when ``ModuleNotFoundError`` is raised.
     severity: str, either of "error", "warning" or "none"
         behaviour for raising errors or warnings
-        "error" - raises a `ModuleNotFound` if mlflow-related packages are not found.
+        "error" - raises a ``ModuleNotFound`` if mlflow-related packages are not found.
         "warning" - raises a warning message if any mlflow-related package is not
             installed also returns False. In case all packages are present,
             returns True.
diff --git a/sktime/utils/multiindex.py b/sktime/utils/multiindex.py
index 38d23175b..a02acc0c0 100644
--- a/sktime/utils/multiindex.py
+++ b/sktime/utils/multiindex.py
@@ -4,6 +4,7 @@
 __author__ = ["fkiraly"]
 __all__ = []
 
+import numpy as np
 import pandas as pd
 
 
@@ -83,3 +84,46 @@ def rename_multiindex(idx, feature_names_out, idx_name="index"):
             'must be one of "flat", "multiindex", "original", "auto", '
             f"but found {feature_names_out}"
         )
+
+
+def apply_split(y, iloc_ix):
+    """Generate iloc indices to split according to first MultiIndex level.
+
+    Applies iloc_ix to the leftmost (0-th) level of a MultiIndex y.
+
+    Parameters
+    ----------
+    y : pd.MultiIndex or pd.Index
+        Index to split, coerced to MultiIndex if not already
+    iloc_ix: 1D np.ndarray of integer
+        iloc indices to apply to the first level of y
+
+    Returns
+    -------
+    y_iloc : ndarray
+        iloc indices for y after applying iloc_ix to the first level
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import pandas as pd
+    >>> from sktime.utils.multiindex import apply_split
+    >>> y = pd.MultiIndex.from_tuples([(0, 0), (0, 1), (1, 0), (1, 1)])
+    >>> iloc_ix = np.array([1, 0])
+    >>> apply_split(y, iloc_ix)
+    array([2, 3, 0, 1])
+    """
+    if not isinstance(y, pd.MultiIndex):
+        zeros = [0] * len(y)
+        y = pd.MultiIndex.from_arrays([zeros, y])
+
+    if not isinstance(iloc_ix, np.ndarray):
+        iloc_ix = np.array(iloc_ix)
+
+    inst_ix = y.droplevel(-1).unique()
+    iloc_ixer = pd.DataFrame(pd.RangeIndex(len(y)), index=y)
+
+    y_loc = inst_ix[np.array(iloc_ix)]
+    y_np = [iloc_ixer.loc[x].to_numpy().flatten() for x in y_loc]
+    y_iloc = np.concatenate(y_np)
+    return y_iloc


Test patch:
diff --git a/sktime/detection/_datatypes/test/__init__.py b/sktime/detection/_datatypes/test/__init__.py
new file mode 100644
index 000000000..41ca60652
--- /dev/null
+++ b/sktime/detection/_datatypes/test/__init__.py
@@ -0,0 +1,1 @@
+"""Tests for utilities for checks and conversions for detection outputs."""
diff --git a/sktime/detection/_datatypes/test/test_convert.py b/sktime/detection/_datatypes/test/test_convert.py
new file mode 100644
index 000000000..348fcfe73
--- /dev/null
+++ b/sktime/detection/_datatypes/test/test_convert.py
@@ -0,0 +1,47 @@
+"""Tests for detection output converters."""
+
+import pandas as pd
+import pytest
+
+from sktime.detection._datatypes._convert import (
+    _convert_points_to_segments,
+    _convert_segments_to_points,
+)
+from sktime.detection._datatypes._examples import (
+    _get_example_points_2,
+    _get_example_points_3,
+    _get_example_segments_2,
+    _get_example_segments_3,
+)
+from sktime.tests.test_switch import run_test_module_changed
+
+
+@pytest.mark.skipif(
+    not run_test_module_changed("sktime.detection._datatypes"),
+    reason="Test only runs when module changed",
+)
+def test_convert_points_to_segments():
+    """Test _convert_points_to_segments on an example."""
+    points_df = _get_example_points_2()
+    segments_df_no_lab = _convert_points_to_segments(points_df, len_X=10)
+    segments_df_w_lab = _convert_points_to_segments(
+        points_df, len_X=10, include_labels=True
+    )
+    expected_segments_df_w_lab = _get_example_segments_2()
+    expected_segments_df_no_lab = expected_segments_df_w_lab.drop(columns=["labels"])
+
+    pd.testing.assert_frame_equal(segments_df_no_lab, expected_segments_df_no_lab)
+    pd.testing.assert_frame_equal(segments_df_w_lab, expected_segments_df_w_lab)
+
+
+@pytest.mark.skipif(
+    not run_test_module_changed("sktime.detection._datatypes"),
+    reason="Test only runs when module changed",
+)
+def test_convert_segments_to_points():
+    """Test _convert_points_to_segments on an example."""
+    seg_df = _get_example_segments_3()
+    points_df_expected = _get_example_points_3()
+    points_df = _convert_segments_to_points(seg_df, len_X=10)
+
+    pd.testing.assert_frame_equal(points_df, points_df_expected)
diff --git a/sktime/detection/tests/test_all_detectors.py b/sktime/detection/tests/test_all_detectors.py
index 71948219e..13ae66282 100644
--- a/sktime/detection/tests/test_all_detectors.py
+++ b/sktime/detection/tests/test_all_detectors.py
@@ -5,6 +5,7 @@ __all__ = []
 
 import pandas as pd
 
+from sktime.detection._datatypes._check import _is_valid_detection
 from sktime.tests.test_all_estimators import BaseFixtureGenerator, QuickTester
 from sktime.utils._testing.detection import make_detection_problem
 from sktime.utils.validation.detection import check_learning_type, check_task
@@ -54,9 +55,21 @@ class TestAllDetectors(DetectorFixtureGenerator, QuickTester):
         X_test = make_detection_problem(
             n_timepoints=10, estimator_type=estimator.get_tag("distribution_type")
         )
-        y_test = estimator.predict(X_test)
-        assert isinstance(y_test, pd.DataFrame)
-        assert isinstance(y_test.index, pd.RangeIndex)
+        y_pred = estimator.predict(X_test)
+
+        detector_type = estimator.get_tag("task")
+        if detector_type in ["anomaly_detection", "change_point_detection"]:
+            expected_y_pred_type = "points"
+        elif detector_type == "segmentation":
+            expected_y_pred_type = "segments"
+        else:
+            raise ValueError(
+                f"Unknown task tag of {estimator_instance}: {detector_type}, "
+                "expected one of ['anomaly_detection', 'change_point_detection', "
+                f"'segmentation'], but found {detector_type}"
+            )
+
+        assert _is_valid_detection(y_pred, type=expected_y_pred_type), y_pred
 
     def test_transform_output_type(self, estimator_instance):
         """Test output type for the transform method."""
@@ -84,10 +97,7 @@ class TestAllDetectors(DetectorFixtureGenerator, QuickTester):
             estimator_type=estimator_instance.get_tag("distribution_type"),
         )
         y_pred = estimator_instance.predict_points(X_test)
-        assert isinstance(y_pred, pd.DataFrame)
-        assert isinstance(y_pred.index, pd.RangeIndex)
-        assert "ilocs" in y_pred.columns
-        assert pd.api.types.is_integer_dtype(y_pred["ilocs"].dtype), y_pred
+        assert _is_valid_detection(y_pred, type="points"), y_pred
 
     def test_predict_segments(self, estimator_instance):
         X_train = make_detection_problem(
@@ -101,12 +111,7 @@ class TestAllDetectors(DetectorFixtureGenerator, QuickTester):
             estimator_type=estimator_instance.get_tag("distribution_type"),
         )
         y_pred = estimator_instance.predict_segments(X_test)
-        assert isinstance(y_pred, pd.DataFrame)
-        assert isinstance(y_pred.index, pd.RangeIndex)
-        assert "ilocs" in y_pred.columns
-        ilocs_dtype = y_pred["ilocs"].dtype
-        assert isinstance(y_pred["ilocs"].dtype, pd.IntervalDtype)
-        assert pd.api.types.is_integer_dtype(ilocs_dtype.subtype)
+        assert _is_valid_detection(y_pred, type="segments"), y_pred
 
     def test_detector_tags(self, estimator_class):
         """Check the learning_type and task tags are valid."""
diff --git a/sktime/detection/tests/test_base.py b/sktime/detection/tests/test_base.py
index 4b9a8b129..101998826 100644
--- a/sktime/detection/tests/test_base.py
+++ b/sktime/detection/tests/test_base.py
@@ -11,6 +11,7 @@ from sktime.detection.base import BaseDetector
 from sktime.tests.test_switch import run_test_for_class
 
 
+@pytest.mark.xfail(reason="Changed interface, need to update test")
 @pytest.mark.skipif(
     not run_test_for_class(BaseDetector),
     reason="run test only if softdeps are present and incrementally (if requested)",
@@ -118,6 +119,7 @@ def test_change_points_to_segments(change_points, expected_segments, start, end)
     testing.assert_series_equal(actual_segments, expected_segments)
 
 
+@pytest.mark.xfail(reason="Changed interface, need to update test")
 @pytest.mark.skipif(
     not run_test_for_class(BaseDetector),
     reason="run test only if softdeps are present and incrementally (if requested)",
@@ -142,6 +144,7 @@ def test_segments_to_change_points(segments, expected_change_points):
     )
 
 
+@pytest.mark.xfail(reason="Changed interface, need to update test")
 @pytest.mark.skipif(
     not run_test_for_class(BaseDetector),
     reason="run test only if softdeps are present and incrementally (if requested)",
diff --git a/sktime/detection/tests/test_hmm.py b/sktime/detection/tests/test_hmm.py
index 5fb082772..b3d13b5da 100644
--- a/sktime/detection/tests/test_hmm.py
+++ b/sktime/detection/tests/test_hmm.py
@@ -42,7 +42,7 @@ def test_hmm_basic_gauss():
         transition_matrix[ind, ind] = 1 - (1 / (s_len / 14))
     gauss_test = HMM(emi_funcs, transition_matrix)
     gauss_test.fit(gauss_data)
-    predicted_labels = gauss_test.predict(gauss_data)
+    predicted_labels = gauss_test.transform(gauss_data)
     predicted_labels = predicted_labels.iloc[:, 0]
     assert len(predicted_labels == labels) >= 0.95 * len(predicted_labels)
 
@@ -94,7 +94,7 @@ def test_hmm_behaves_as_expected_on_simple_input():
     # generate synthetic data (or of course use your own!)
     obs = asarray([3.7, 3.2, 3.4, 3.6, -5.1, -5.2, -4.9])
     hmm_est = hmm_est.fit(obs)
-    labels = hmm_est.predict(obs)
+    labels = hmm_est.transform(obs)
     labels = labels.values.flatten()
     ground_truth = asarray([0, 0, 0, 0, 1, 1, 1])
     assert array_equal(labels, ground_truth)
diff --git a/sktime/detection/tests/test_hmm_learn.py b/sktime/detection/tests/test_hmm_learn.py
index c8e611f7b..a18e3943f 100644
--- a/sktime/detection/tests/test_hmm_learn.py
+++ b/sktime/detection/tests/test_hmm_learn.py
@@ -27,7 +27,7 @@ def test_GaussianHMM_wrapper():
     hmmlearn_model.fit(X=data)
     sktime_model.fit(X=data)
     hmmlearn_predict = hmmlearn_model.predict(X=data)
-    sktime_predict = sktime_model.predict(X=data).values.flatten()
+    sktime_predict = sktime_model.transform(X=data).values.flatten()
     assert array_equal(hmmlearn_predict, sktime_predict)
 
 
@@ -48,7 +48,7 @@ def test_GMMHMM_wrapper():
     hmmlearn_model.fit(X=data)
     sktime_model.fit(X=data)
     hmmlearn_predict = hmmlearn_model.predict(X=data)
-    sktime_predict = sktime_model.predict(X=data).values.flatten()
+    sktime_predict = sktime_model.transform(X=data).values.flatten()
     assert array_equal(hmmlearn_predict, sktime_predict)
 
 
@@ -69,5 +69,5 @@ def test_PoissonHMM_wrapper():
     hmmlearn_model.fit(X=data)
     sktime_model.fit(X=data)
     hmmlearn_predict = hmmlearn_model.predict(X=data)
-    sktime_predict = sktime_model.predict(X=data).values.flatten()
+    sktime_predict = sktime_model.transform(X=data).values.flatten()
     assert array_equal(hmmlearn_predict, sktime_predict)
diff --git a/sktime/forecasting/compose/tests/test_hierarchy_ensemble.py b/sktime/forecasting/compose/tests/test_hierarchy_ensemble.py
index 1f6d5c928..760818b69 100644
--- a/sktime/forecasting/compose/tests/test_hierarchy_ensemble.py
+++ b/sktime/forecasting/compose/tests/test_hierarchy_ensemble.py
@@ -212,3 +212,34 @@ def test_level_one_data(forecasters, default):
         def_pred = def_frcstr.predict()
         msg = "Node default predictions do not match"
         assert np.all(actual_pred.loc[def_pred.index] == def_pred), msg
+
+
+@pytest.mark.skipif(
+    not run_test_for_class(HierarchyEnsembleForecaster),
+    reason="run test only if softdeps are present and incrementally (if requested)",
+)
+def test_get_fitted_params():
+    """Tests that get_fitted_params works as expected.
+
+    Checks absence of bug #7418.
+    """
+    from sktime.forecasting.compose import HierarchyEnsembleForecaster
+    from sktime.forecasting.naive import NaiveForecaster
+    from sktime.forecasting.trend import PolynomialTrendForecaster, TrendForecaster
+    from sktime.utils._testing.hierarchical import _bottom_hier_datagen
+
+    y = _bottom_hier_datagen(no_bottom_nodes=7, no_levels=2, random_seed=123)
+
+    forecasters = [
+        ("naive", NaiveForecaster(), 0),
+        ("trend", TrendForecaster(), 1),
+    ]
+    forecaster = HierarchyEnsembleForecaster(
+        forecasters=forecasters,
+        by="level",
+        default=PolynomialTrendForecaster(degree=2),
+    )
+    forecaster.fit(y, fh=[1, 2, 3])
+
+    assert forecaster.get_fitted_params()["naive"].is_fitted
+    assert forecaster.get_fitted_params()["trend"].is_fitted
diff --git a/sktime/performance_metrics/detection/tests/__init__.py b/sktime/performance_metrics/detection/tests/__init__.py
new file mode 100644
index 000000000..951487535
--- /dev/null
+++ b/sktime/performance_metrics/detection/tests/__init__.py
@@ -0,0 +1,1 @@
+"""Tests for detection metrics."""
diff --git a/sktime/performance_metrics/detection/tests/test_all_metrics_detection.py b/sktime/performance_metrics/detection/tests/test_all_metrics_detection.py
new file mode 100644
index 000000000..414324fd5
--- /dev/null
+++ b/sktime/performance_metrics/detection/tests/test_all_metrics_detection.py
@@ -0,0 +1,76 @@
+"""Tests for all sktime detection metrics."""
+
+import pandas as pd
+import pytest
+
+from sktime.tests.test_all_estimators import BaseFixtureGenerator, QuickTester
+
+
+class DetectionMetricFixtureGenerator(BaseFixtureGenerator):
+    """Fixture generator for time series detection metric tests.
+
+    Fixtures parameterized
+    ----------------------
+    estimator_class: estimator inheriting from BaseObject
+        ranges over estimator classes not excluded by EXCLUDE_ESTIMATORS, EXCLUDED_TESTS
+    estimator_instance: instance of estimator inheriting from BaseObject
+        ranges over estimator classes not excluded by EXCLUDE_ESTIMATORS, EXCLUDED_TESTS
+        instances are generated by create_test_instance class method
+    scenario: instance of TestScenario
+        ranges over all scenarios returned by retrieve_scenarios
+    """
+
+    # note: this should be separate from TestAllDetectionMetrics
+    #   additional fixtures, parameters, etc should be added here
+    #   TestAllDetectionMetrics should contain the tests only
+
+    estimator_type_filter = "metric_detection"
+
+    fixture_sequence = [
+        "estimator_class",
+        "estimator_instance",
+        "fitted_estimator",
+        "scenario",
+    ]
+
+
+class TestAllDetectionMetrics(DetectionMetricFixtureGenerator, QuickTester):
+    """Module level tests for all sktime detection metrics."""
+
+    def test_evaluate_output(self, estimator_instance):
+        """Test expected output type of evaluate_output."""
+        y_pred = pd.DataFrame({"ilocs": [0, 1, 3, 4, 5]})  # locs are 1, 2, 5, 42, 43
+        y_true = pd.DataFrame({"ilocs": [0, 2, 3]})  # locs are 1, 4, 5
+        X = pd.DataFrame({"foo": [8, 4, 3, 7, 10, 12]}, index=[1, 2, 4, 5, 42, 43])
+
+        metric = estimator_instance
+
+        requires_X = metric.get_tag("requires_X")
+        requires_y_true = metric.get_tag("requires_y_true")
+
+        # test full signature
+        loss = metric(y_true, y_pred, X)
+        assert isinstance(loss, float)
+
+        # test without X
+        if not requires_X:
+            loss_no_X = metric(y_true, y_pred)
+            assert isinstance(loss_no_X, float)
+        else:
+            with pytest.raises(TypeError):
+                metric(y_true, y_pred)
+
+        # test without y_true
+        if not requires_y_true:
+            loss_no_y_true_kwarg = metric(y_pred=y_pred)
+            assert isinstance(loss_no_y_true_kwarg, float)
+
+            loss_no_y_true_posarg = metric(y_pred)
+            assert isinstance(loss_no_y_true_posarg, float)
+
+            assert loss_no_y_true_kwarg == loss_no_y_true_posarg
+        else:
+            with pytest.raises(TypeError):
+                metric(y_pred)
+            with pytest.raises(TypeError):
+                metric(y_pred=y_pred)
diff --git a/sktime/performance_metrics/detection/tests/test_base.py b/sktime/performance_metrics/detection/tests/test_base.py
new file mode 100644
index 000000000..1de740f8a
--- /dev/null
+++ b/sktime/performance_metrics/detection/tests/test_base.py
@@ -0,0 +1,38 @@
+"""Tests for base class boilerplate."""
+
+import pandas as pd
+import pytest
+
+from sktime.detection._datatypes._examples import (
+    _get_example_points_2,
+    _get_example_points_3,
+    _get_example_segments_2,
+    _get_example_segments_3,
+)
+from sktime.performance_metrics.detection._chamfer import DirectedChamfer
+from sktime.tests.test_switch import run_test_module_changed
+
+
+@pytest.mark.skipif(
+    not run_test_module_changed("sktime.performance_metrics.detection"),
+    reason="run test only if detection module changed",
+)
+def test_input_coercions():
+    """Test that segments are coerced for point metrics."""
+    y_pred_seg = _get_example_segments_2()
+    y_true_seg = _get_example_segments_3()
+
+    y_pred_pts = _get_example_points_2()
+    y_true_pts = _get_example_points_3()
+
+    metric = DirectedChamfer()
+
+    loss_seg = metric(y_true_seg, y_pred_seg)
+    loss_pts = metric(y_true_pts, y_pred_pts)
+    assert loss_seg == loss_pts
+
+    X = pd.DataFrame({"range10": range(10)})
+
+    loss_seg_X = metric(y_true_seg, y_pred_seg, X)
+    loss_pts_X = metric(y_true_pts, y_pred_pts, X)
+    assert loss_seg_X == loss_pts_X
diff --git a/sktime/performance_metrics/detection/tests/test_chamfer.py b/sktime/performance_metrics/detection/tests/test_chamfer.py
new file mode 100644
index 000000000..90a8d3017
--- /dev/null
+++ b/sktime/performance_metrics/detection/tests/test_chamfer.py
@@ -0,0 +1,33 @@
+"""Tests for the directed Chamfer distance."""
+
+import pandas as pd
+import pytest
+
+from sktime.performance_metrics.detection._chamfer import DirectedChamfer
+from sktime.tests.test_switch import run_test_for_class
+
+
+@pytest.mark.skipif(
+    not run_test_for_class(DirectedChamfer),
+    reason="run test only if softdeps are present and incrementally (if requested)",
+)
+def test_chamfer():
+    """Test the directed Chamfer distance."""
+    y_pred = pd.DataFrame({"ilocs": [0, 1, 3, 4, 5]})  # locs are 1, 2, 5, 42, 43
+    y_true = pd.DataFrame({"ilocs": [0, 2, 3]})  # locs are 1, 4, 5
+    X = pd.DataFrame({"foo": [8, 4, 3, 7, 10, 12]}, index=[1, 2, 4, 5, 42, 43])
+
+    metric = DirectedChamfer()
+
+    loss1 = metric(y_true, y_pred)
+    assert isinstance(loss1, float)
+    # closest ilocs are 0, 0, 3, 3, 3
+    # absolute distance is 0 + 1 + 0 + 1 + 2 = 4
+    assert loss1 == 4
+
+    loss2 = metric(y_true, y_pred, X)
+    assert isinstance(loss2, float)
+    # predicted locs are 1, 2, 5, 42, 43
+    # closest locs are 1, 1, 5, 5, 5
+    # distances are 0 + 1 + 0 + 37 + 38 = 76
+    assert loss2 == 76
diff --git a/sktime/performance_metrics/detection/tests/test_hausdorff.py b/sktime/performance_metrics/detection/tests/test_hausdorff.py
new file mode 100644
index 000000000..a1954c1e2
--- /dev/null
+++ b/sktime/performance_metrics/detection/tests/test_hausdorff.py
@@ -0,0 +1,33 @@
+"""Tests for the directed Hausdorff distance."""
+
+import pandas as pd
+import pytest
+
+from sktime.performance_metrics.detection._hausdorff import DirectedHausdorff
+from sktime.tests.test_switch import run_test_for_class
+
+
+@pytest.mark.skipif(
+    not run_test_for_class(DirectedHausdorff),
+    reason="run test only if softdeps are present and incrementally (if requested)",
+)
+def test_hausdorff():
+    """Test the directed Hausdorff distance."""
+    y_pred = pd.DataFrame({"ilocs": [0, 1, 3, 4, 5]})  # locs are 1, 2, 5, 42, 43
+    y_true = pd.DataFrame({"ilocs": [0, 2, 3]})  # locs are 1, 4, 5
+    X = pd.DataFrame({"foo": [8, 4, 3, 7, 10, 12]}, index=[1, 2, 4, 5, 42, 43])
+
+    metric = DirectedHausdorff()
+
+    loss1 = metric(y_true, y_pred)
+    assert isinstance(loss1, float)
+    # closest ilocs are 0, 0, 3, 3, 3
+    # absolute distance is max(0, 1, 0, 1, 2) = 2
+    assert loss1 == 2
+
+    loss2 = metric(y_true, y_pred, X)
+    assert isinstance(loss2, float)
+    # predicted locs are 1, 2, 5, 42, 43
+    # closest locs are 1, 1, 5, 5, 5
+    # absolute distance is max(0, 1, 0, 37, 38) = 38
+    assert loss2 == 38
diff --git a/sktime/tests/_config.py b/sktime/tests/_config.py
index c3b3fd8ac..511a236cc 100644
--- a/sktime/tests/_config.py
+++ b/sktime/tests/_config.py
@@ -246,19 +246,6 @@ EXCLUDED_TESTS = {
         "test_persistence_via_pickle",
         "test_save_estimators_to_file",
     ],
-    # The following detectors are not interface compliant. See PR 6958
-    "PoissonHMM": [
-        "test_predict_points",
-        "test_predict_segments",
-        "test_transform_output_type",
-        "test_output_type",
-    ],
-    "HMM": [
-        "test_predict_points",
-        "test_predict_segments",
-        "test_transform_output_type",
-        "test_output_type",
-    ],
     "ClaSPSegmentation": [
         "test_predict_points",
         "test_predict_segments",
@@ -287,18 +274,6 @@ EXCLUDED_TESTS = {
         "test_predict_segments",
         "test_transform_output_type",
     ],
-    "GaussianHMM": [
-        "test_predict_points",
-        "test_predict_segments",
-        "test_transform_output_type",
-        "test_output_type",
-    ],
-    "GMMHMM": [
-        "test_predict_points",
-        "test_predict_segments",
-        "test_transform_output_type",
-        "test_output_type",
-    ],
 }
 
 # exclude tests but keyed by test name
@@ -340,7 +315,6 @@ EXCLUDED_TESTS_BY_TEST = {
         "FittedParamExtractor",
         "ForecastingOptunaSearchCV",
         "FreshPRINCE",
-        "GaussianHMM",
         "GreedyGaussianSegmentation",
         "HCrystalBallAdapter",
         "HIVECOTEV1",
@@ -380,7 +354,6 @@ EXCLUDED_TESTS_BY_TEST = {
         "PlateauFinder",
         "PluginParamsForecaster",
         "PluginParamsTransformer",
-        "PoissonHMM",
         "Prophet",
         "ProphetPiecewiseLinearTrendForecaster",
         "Prophetverse",
diff --git a/sktime/tests/test_class_register.py b/sktime/tests/test_class_register.py
index 3aae18ba2..275561720 100644
--- a/sktime/tests/test_class_register.py
+++ b/sktime/tests/test_class_register.py
@@ -20,51 +20,18 @@ def get_test_class_registry():
         test class registry
         keys are scitypes, values are test classes TestAll[Scitype]
     """
-    from sktime.alignment.tests.test_all_aligners import TestAllAligners
-    from sktime.classification.early_classification.tests.test_all_early_classifiers import (  # noqa E501
-        TestAllEarlyClassifiers,
-    )
-    from sktime.classification.tests.test_all_classifiers import TestAllClassifiers
-    from sktime.detection.tests.test_all_detectors import TestAllDetectors
-    from sktime.dists_kernels.tests.test_all_dist_kernels import (
-        TestAllPairwiseTransformers,
-        TestAllPanelTransformers,
-    )
-    from sktime.forecasting.tests.test_all_forecasters import (
-        TestAllForecasters,
-        TestAllGlobalForecasters,
-    )
-    from sktime.param_est.tests.test_all_param_est import TestAllParamFitters
-    from sktime.regression.tests.test_all_regressors import TestAllRegressors
-    from sktime.split.tests.test_all_splitters import TestAllSplitters
-    from sktime.tests.test_all_estimators import TestAllEstimators, TestAllObjects
-    from sktime.transformations.tests.test_all_transformers import TestAllTransformers
-
-    testclass_dict = dict()
-    # every object in sktime inherits from BaseObject
-    # "object" tests are run for all objects
-    testclass_dict["object"] = TestAllObjects
-    # fittable objects inherit from BaseEstimator
-    # "estimator" tests are run for all estimators
-    # estimators are also objects
-    testclass_dict["estimator"] = TestAllEstimators
-    # more specific base classes
-    # these inherit either from BaseEstimator or BaseObject,
-    # so also imply estimator and object tests, or only object tests
-    testclass_dict["aligner"] = TestAllAligners
-    testclass_dict["classifier"] = TestAllClassifiers
-    testclass_dict["detector"] = TestAllDetectors
-    testclass_dict["early_classifier"] = TestAllEarlyClassifiers
-    testclass_dict["forecaster"] = TestAllForecasters
-    testclass_dict["global_forecaster"] = TestAllGlobalForecasters
-    testclass_dict["param_est"] = TestAllParamFitters
-    testclass_dict["regressor"] = TestAllRegressors
-    # todo 1.0.0 - remove series-annotator
-    testclass_dict["series-annotator"] = TestAllDetectors
-    testclass_dict["splitter"] = TestAllSplitters
-    testclass_dict["transformer"] = TestAllTransformers
-    testclass_dict["transformer-pairwise"] = TestAllPairwiseTransformers
-    testclass_dict["transformer-pairwise-panel"] = TestAllPanelTransformers
+    from sktime.registry._base_classes import _get_base_classes
+
+    base_classes = _get_base_classes()
+
+    testclass_dict = {}
+
+    for base_class in base_classes:
+        scitype_str = base_class.get_class_tag("scitype_name")
+        test_class = base_class.get_test_class()
+
+        if test_class is not None:
+            testclass_dict[scitype_str] = test_class
 
     return testclass_dict
 
diff --git a/sktime/tests/test_softdeps.py b/sktime/tests/test_softdeps.py
index 599dd7fb4..800623058 100644
--- a/sktime/tests/test_softdeps.py
+++ b/sktime/tests/test_softdeps.py
@@ -83,9 +83,7 @@ def is_soft_dep_missing_message(msg):
     missing_version_msg = "to be present in the python environment, with version"
     cond1 = missing_version_msg in msg
     # message if dependency is missing entirely
-    missing_dep_entirely_msg = (
-        "is a soft dependency and not included in the base sktime installation"
-    )
+    missing_dep_entirely_msg = "requires package"
     cond2 = missing_dep_entirely_msg in msg
     # special message for deep learning dependencies
     error_msg_dl = "required for deep learning"
diff --git a/sktime/utils/_testing/tests/test_check_imports.py b/sktime/utils/_testing/tests/test_check_imports.py
index 52ea182ba..4c1210848 100644
--- a/sktime/utils/_testing/tests/test_check_imports.py
+++ b/sktime/utils/_testing/tests/test_check_imports.py
@@ -5,8 +5,9 @@ from sktime.utils.dependencies import _check_soft_dependencies
 
 def test_check_soft_dependencies_raises_error():
     """Test the _check_soft_dependencies() function."""
-    with pytest.raises(ModuleNotFoundError, match=r".* soft dependency .*"):
-        _check_soft_dependencies("unavailable_module")
+    MODULE = "unavailable_module"
+    with pytest.raises(ModuleNotFoundError, match=r".* requires package .*"):
+        _check_soft_dependencies(MODULE)
 
-    with pytest.raises(ModuleNotFoundError, match=r".* soft dependency .*"):
-        _check_soft_dependencies("unavailable_module_1", "unavailable_module_2")
+    with pytest.raises(ModuleNotFoundError, match=r".* requires package .*"):
+        _check_soft_dependencies(MODULE + "_1", MODULE + "_2")
diff --git a/sktime/utils/tests/test_multiindex.py b/sktime/utils/tests/test_multiindex.py
index 2023f4d5d..b2b5bbc56 100644
--- a/sktime/utils/tests/test_multiindex.py
+++ b/sktime/utils/tests/test_multiindex.py
@@ -1,10 +1,11 @@
 """Testing multiindex utilities."""
 
+import numpy as np
 import pandas as pd
 import pytest
 
 from sktime.tests.test_switch import run_test_module_changed
-from sktime.utils.multiindex import flatten_multiindex, rename_multiindex
+from sktime.utils.multiindex import apply_split, flatten_multiindex, rename_multiindex
 
 
 @pytest.mark.skipif(
@@ -79,3 +80,21 @@ def test_rename_multiindex():
 
     assert isinstance(orig, pd.Index)
     assert (expected_orig == orig).all()
+
+
+@pytest.mark.skipif(
+    not run_test_module_changed(["sktime.utils.multiindex"]),
+    reason="Run if multiindex module has changed.",
+)
+def test_apply_split():
+    """Test apply_split() and check shapes"""
+    mi = pd.MultiIndex.from_product([["a", "b"], [0, 42]])
+
+    iloc_ix = [1]
+
+    result = apply_split(mi, iloc_ix)
+
+    expected = np.array([2, 3])
+
+    assert isinstance(result, np.ndarray)
+    assert np.array_equal(result, expected)