Repository: WenjieDu/PyPOTS (language: python)

Issue:
Update tsdb.load_dataset to tsdb.load in doc of load_specific_dataset
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Fixing # (issue) None

I’ve seen `load_specific_dataset` has only been added back temporarily in v0.7.1, and actually BenchPOTS will be the preferred way in the future. So this might not be too important. But got a bit confused when reading this and realized it’s been renamed from `tsdb.load_data` to `tsdb.load`.

Let me know if I have got this wrong!

Best,

## Before submitting

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- [x] This PR is made to fix a typo or improve the docs (you can dismiss the other checks if this is the case).
- [ ] Was this discussed/approved via a GitHub issue? Please add a link to it if that's the case.
- [ ] I have commented my code, particularly in hard-to-understand areas.
- [ ] I have written necessary tests and already run them locally.



Discussion / hints:

Fix patch:
diff --git a/.idea/icon.png b/.idea/icon.png
new file mode 100644
index 00000000..0a58e1cc
Binary files /dev/null and b/.idea/icon.png differ
diff --git a/README.md b/README.md
index fb77cedd..95e74695 100644
--- a/README.md
+++ b/README.md
@@ -113,47 +113,48 @@ The task types are abbreviated as follows:
 **`ANOD`**: Anomaly Detection.
 The paper references and links are all listed at the bottom of this file.
 
-| **Type**      | **Algo**                    | **IMPU** | **FORE** | **CLAS** | **CLUS** | **ANOD** | **Year - Venue**   |
-|:--------------|:----------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:-------------------|
-| LLM           | Gungnir 🚀 [^36]            |    ✅     |    ✅     |    ✅     |    ✅     |    ✅     | `Later in 2024`    |
-| Neural Net    | ImputeFormer🧑‍🔧[^34]      |    ✅     |          |          |          |          | `2024 - KDD`       |
-| Neural Net    | iTransformer🧑‍🔧[^24]      |    ✅     |          |          |          |          | `2024 - ICLR`      |
-| Neural Net    | SAITS[^1]                   |    ✅     |          |          |          |          | `2023 - ESWA`      |
-| Neural Net    | FreTS🧑‍🔧[^23]             |    ✅     |          |          |          |          | `2023 - NeurIPS`   |
-| Neural Net    | Koopa🧑‍🔧[^29]             |    ✅     |          |          |          |          | `2023 - NeurIPS`   |
-| Neural Net    | Crossformer🧑‍🔧[^16]       |    ✅     |          |          |          |          | `2023 - ICLR`      |
-| Neural Net    | TimesNet[^14]               |    ✅     |          |          |          |          | `2023 - ICLR`      |
-| Neural Net    | PatchTST🧑‍🔧[^18]          |    ✅     |          |          |          |          | `2023 - ICLR`      |
-| Neural Net    | ETSformer🧑‍🔧[^19]         |    ✅     |          |          |          |          | `2023 - ICLR`      |
-| Neural Net    | MICN🧑‍🔧[^27]              |    ✅     |          |          |          |          | `2023 - ICLR`      |
-| Neural Net    | DLinear🧑‍🔧[^17]           |    ✅     |          |          |          |          | `2023 - AAAI`      |
-| Neural Net    | TiDE🧑‍🔧[^28]              |    ✅     |          |          |          |          | `2023 - TMLR`      |
-| Neural Net    | SCINet🧑‍🔧[^30]            |    ✅     |          |          |          |          | `2022 - NeurIPS`   |
-| Neural Net    | Nonstationary Tr.🧑‍🔧[^25] |    ✅     |          |          |          |          | `2022 - NeurIPS`   |
-| Neural Net    | FiLM🧑‍🔧[^22]              |    ✅     |          |          |          |          | `2022 - NeurIPS`   |
-| Neural Net    | RevIN_SCINet🧑‍🔧[^31]      |    ✅     |          |          |          |          | `2022 - ICLR`      |
-| Neural Net    | Pyraformer🧑‍🔧[^26]        |    ✅     |          |          |          |          | `2022 - ICLR`      |
-| Neural Net    | Raindrop[^5]                |          |          |    ✅     |          |          | `2022 - ICLR`      |
-| Neural Net    | FEDformer🧑‍🔧[^20]         |    ✅     |          |          |          |          | `2022 - ICML`      |
-| Neural Net    | Autoformer🧑‍🔧[^15]        |    ✅     |          |          |          |          | `2021 - NeurIPS`   |
-| Neural Net    | CSDI[^12]                   |    ✅     |    ✅     |          |          |          | `2021 - NeurIPS`   |
-| Neural Net    | Informer🧑‍🔧[^21]          |    ✅     |          |          |          |          | `2021 - AAAI`      |
-| Neural Net    | US-GAN[^10]                 |    ✅     |          |          |          |          | `2021 - AAAI`      |
-| Neural Net    | CRLI[^6]                    |          |          |          |    ✅     |          | `2021 - AAAI`      |
-| Probabilistic | BTTF[^8]                    |          |    ✅     |          |          |          | `2021 - TPAMI`     |
-| Neural Net    | StemGNN🧑‍🔧[^33]           |    ✅     |          |          |          |          | `2020 - NeurIPS`   |
-| Neural Net    | Reformer🧑‍🔧[^32]          |    ✅     |          |          |          |          | `2020 - ICLR`      |
-| Neural Net    | GP-VAE[^11]                 |    ✅     |          |          |          |          | `2020 - AISTATS`   |
-| Neural Net    | VaDER[^7]                   |          |          |          |    ✅     |          | `2019 - GigaSci.`  |
-| Neural Net    | M-RNN[^9]                   |    ✅     |          |          |          |          | `2019 - TBME`      |
-| Neural Net    | BRITS[^3]                   |    ✅     |          |    ✅     |          |          | `2018 - NeurIPS`   |
-| Neural Net    | GRU-D[^4]                   |    ✅     |          |    ✅     |          |          | `2018 - Sci. Rep.` |
-| Neural Net    | TCN🧑‍🔧[^35]               |    ✅     |          |          |          |          | `2018 - arXiv`     |
-| Neural Net    | Transformer🧑‍🔧[^2]        |    ✅     |          |          |          |          | `2017 - NeurIPS`   |
-| Naive         | Lerp                        |    ✅     |          |          |          |          |                    |
-| Naive         | LOCF/NOCB                   |    ✅     |          |          |          |          |                    |
-| Naive         | Mean                        |    ✅     |          |          |          |          |                    |
-| Naive         | Median                      |    ✅     |          |          |          |          |                    |
+| **Type**      | **Algo**                                                                                                                         | **IMPU** | **FORE** | **CLAS** | **CLUS** | **ANOD** | **Year - Venue**   |
+|:--------------|:---------------------------------------------------------------------------------------------------------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:-------------------|
+| LLM           | <a href="https://time-series.ai"><img src="https://time-series.ai/static/figs/robot.svg" width="26px"> Time-Series.AI</a>  [^36] |    ✅     |    ✅     |    ✅     |    ✅     |    ✅     | `Later in 2024`    |
+| Neural Net    | TimeMixer[^37]                                                                                                                   |    ✅     |          |          |          |          | `2024 - ICLR`      |
+| Neural Net    | iTransformer🧑‍🔧[^24]                                                                                                           |    ✅     |          |          |          |          | `2024 - ICLR`      |
+| Neural Net    | ImputeFormer🧑‍🔧[^34]                                                                                                           |    ✅     |          |          |          |          | `2024 - KDD`       |
+| Neural Net    | SAITS[^1]                                                                                                                        |    ✅     |          |          |          |          | `2023 - ESWA`      |
+| Neural Net    | FreTS🧑‍🔧[^23]                                                                                                                  |    ✅     |          |          |          |          | `2023 - NeurIPS`   |
+| Neural Net    | Koopa🧑‍🔧[^29]                                                                                                                  |    ✅     |          |          |          |          | `2023 - NeurIPS`   |
+| Neural Net    | Crossformer🧑‍🔧[^16]                                                                                                            |    ✅     |          |          |          |          | `2023 - ICLR`      |
+| Neural Net    | TimesNet[^14]                                                                                                                    |    ✅     |          |          |          |          | `2023 - ICLR`      |
+| Neural Net    | PatchTST🧑‍🔧[^18]                                                                                                               |    ✅     |          |          |          |          | `2023 - ICLR`      |
+| Neural Net    | ETSformer🧑‍🔧[^19]                                                                                                              |    ✅     |          |          |          |          | `2023 - ICLR`      |
+| Neural Net    | MICN🧑‍🔧[^27]                                                                                                                   |    ✅     |          |          |          |          | `2023 - ICLR`      |
+| Neural Net    | DLinear🧑‍🔧[^17]                                                                                                                |    ✅     |          |          |          |          | `2023 - AAAI`      |
+| Neural Net    | TiDE🧑‍🔧[^28]                                                                                                                   |    ✅     |          |          |          |          | `2023 - TMLR`      |
+| Neural Net    | SCINet🧑‍🔧[^30]                                                                                                                 |    ✅     |          |          |          |          | `2022 - NeurIPS`   |
+| Neural Net    | Nonstationary Tr.🧑‍🔧[^25]                                                                                                      |    ✅     |          |          |          |          | `2022 - NeurIPS`   |
+| Neural Net    | FiLM🧑‍🔧[^22]                                                                                                                   |    ✅     |          |          |          |          | `2022 - NeurIPS`   |
+| Neural Net    | RevIN_SCINet🧑‍🔧[^31]                                                                                                           |    ✅     |          |          |          |          | `2022 - ICLR`      |
+| Neural Net    | Pyraformer🧑‍🔧[^26]                                                                                                             |    ✅     |          |          |          |          | `2022 - ICLR`      |
+| Neural Net    | Raindrop[^5]                                                                                                                     |          |          |    ✅     |          |          | `2022 - ICLR`      |
+| Neural Net    | FEDformer🧑‍🔧[^20]                                                                                                              |    ✅     |          |          |          |          | `2022 - ICML`      |
+| Neural Net    | Autoformer🧑‍🔧[^15]                                                                                                             |    ✅     |          |          |          |          | `2021 - NeurIPS`   |
+| Neural Net    | CSDI[^12]                                                                                                                        |    ✅     |    ✅     |          |          |          | `2021 - NeurIPS`   |
+| Neural Net    | Informer🧑‍🔧[^21]                                                                                                               |    ✅     |          |          |          |          | `2021 - AAAI`      |
+| Neural Net    | US-GAN[^10]                                                                                                                      |    ✅     |          |          |          |          | `2021 - AAAI`      |
+| Neural Net    | CRLI[^6]                                                                                                                         |          |          |          |    ✅     |          | `2021 - AAAI`      |
+| Probabilistic | BTTF[^8]                                                                                                                         |          |    ✅     |          |          |          | `2021 - TPAMI`     |
+| Neural Net    | StemGNN🧑‍🔧[^33]                                                                                                                |    ✅     |          |          |          |          | `2020 - NeurIPS`   |
+| Neural Net    | Reformer🧑‍🔧[^32]                                                                                                               |    ✅     |          |          |          |          | `2020 - ICLR`      |
+| Neural Net    | GP-VAE[^11]                                                                                                                      |    ✅     |          |          |          |          | `2020 - AISTATS`   |
+| Neural Net    | VaDER[^7]                                                                                                                        |          |          |          |    ✅     |          | `2019 - GigaSci.`  |
+| Neural Net    | M-RNN[^9]                                                                                                                        |    ✅     |          |          |          |          | `2019 - TBME`      |
+| Neural Net    | BRITS[^3]                                                                                                                        |    ✅     |          |    ✅     |          |          | `2018 - NeurIPS`   |
+| Neural Net    | GRU-D[^4]                                                                                                                        |    ✅     |          |    ✅     |          |          | `2018 - Sci. Rep.` |
+| Neural Net    | TCN🧑‍🔧[^35]                                                                                                                    |    ✅     |          |          |          |          | `2018 - arXiv`     |
+| Neural Net    | Transformer🧑‍🔧[^2]                                                                                                             |    ✅     |          |          |          |          | `2017 - NeurIPS`   |
+| Naive         | Lerp                                                                                                                             |    ✅     |          |          |          |          |                    |
+| Naive         | LOCF/NOCB                                                                                                                        |    ✅     |          |          |          |          |                    |
+| Naive         | Mean                                                                                                                             |    ✅     |          |          |          |          |                    |
+| Naive         | Median                                                                                                                           |    ✅     |          |          |          |          |                    |
 
 💯 Contribute your model right now to increase your research impact! PyPOTS downloads are increasing rapidly (**[300K+ in total and 1K+ daily on PyPI so far](https://www.pepy.tech/projects/pypots)**),
 and your work will be widely used and cited by the community.
@@ -320,7 +321,7 @@ By committing your code, you'll
    [pypots/imputation/template](https://github.com/WenjieDu/PyPOTS/tree/main/pypots/imputation/template)) to quickly start;
 2. become one of [PyPOTS contributors](https://github.com/WenjieDu/PyPOTS/graphs/contributors) and
    be listed as a volunteer developer [on the PyPOTS website](https://pypots.com/about/#volunteer-developers);
-3. get mentioned in our [release notes](https://github.com/WenjieDu/PyPOTS/releases);
+3. get mentioned in PyPOTS [release notes](https://github.com/WenjieDu/PyPOTS/releases);
 
 You can also contribute to PyPOTS by simply staring🌟 this repo to help more people notice it.
 Your star is your recognition to PyPOTS, and it matters!
@@ -394,3 +395,5 @@ PyPOTS community is open, transparent, and surely friendly. Let's work together
 [^35]: Bai, S., Kolter, J. Z., & Koltun, V. (2018). [An empirical evaluation of generic convolutional and recurrent networks for sequence modeling](https://arxiv.org/abs/1803.01271). *arXiv 2018*.
 [^36]: Project Gungnir, the world 1st LLM for time-series multitask modeling, will meet you soon. 🚀 Missing values and variable lengths in your datasets?
        Hard to perform multitask learning with your time series? Not problems no longer. We'll open application for public beta test recently ;-) Follow us, and stay tuned!
+       <a href="https://time-series.ai"><img src="https://time-series.ai/static/figs/robot.svg" width="20px" align="center"> Time-Series.AI</a>
+[^37]: Wang, S., Wu, H., Shi, X., Hu, T., Luo, H., Ma, L., ... & ZHOU, J. (2024). [TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting](https://openreview.net/forum?id=7oLshfEIC2). *ICLR 2024*
diff --git a/README_zh.md b/README_zh.md
index b04dd28d..d3f760cc 100644
--- a/README_zh.md
+++ b/README_zh.md
@@ -99,47 +99,48 @@ PyPOTS当前支持多变量POTS数据的插补，预测，分类，聚类以及
 所以这些模型的输入中不能带有缺失值，无法接受POTS数据作为输入，更加不是插补算法。
 **为了使上述模型能够适用于POTS数据，我们采用了与[SAITS论文](https://arxiv.org/pdf/2202.08516)[^1]中相同的embedding策略和训练方法（ORT+MIT）对它们进行改进**。
 
-| **类型**      | **算法**                      | **插补** | **预测** | **分类** | **聚类** | **异常检测** | **年份 - 刊物**        |
-|:--------------|:----------------------------|:------:|:------:|:------:|:------:|:--------:|:-------------------|
-| LLM           | Gungnir 🚀 [^36]            |    ✅     |    ✅     |    ✅     |    ✅     |    ✅     | `Later in 2024`    |
-| Neural Net    | ImputeFormer🧑‍🔧[^34]      |    ✅     |          |          |          |          | `2024 - KDD`       |
-| Neural Net    | iTransformer🧑‍🔧[^24]      |    ✅     |          |          |          |          | `2024 - ICLR`      |
-| Neural Net    | SAITS[^1]                   |    ✅     |          |          |          |          | `2023 - ESWA`      |
-| Neural Net    | FreTS🧑‍🔧[^23]             |    ✅     |          |          |          |          | `2023 - NeurIPS`   |
-| Neural Net    | Koopa🧑‍🔧[^29]             |    ✅     |          |          |          |          | `2023 - NeurIPS`   |
-| Neural Net    | Crossformer🧑‍🔧[^16]       |    ✅     |          |          |          |          | `2023 - ICLR`      |
-| Neural Net    | TimesNet[^14]               |    ✅     |          |          |          |          | `2023 - ICLR`      |
-| Neural Net    | PatchTST🧑‍🔧[^18]          |    ✅     |          |          |          |          | `2023 - ICLR`      |
-| Neural Net    | ETSformer🧑‍🔧[^19]         |    ✅     |          |          |          |          | `2023 - ICLR`      |
-| Neural Net    | MICN🧑‍🔧[^27]              |    ✅     |          |          |          |          | `2023 - ICLR`      |
-| Neural Net    | DLinear🧑‍🔧[^17]           |    ✅     |          |          |          |          | `2023 - AAAI`      |
-| Neural Net    | TiDE🧑‍🔧[^28]              |    ✅     |          |          |          |          | `2023 - TMLR`      |
-| Neural Net    | SCINet🧑‍🔧[^30]            |    ✅     |          |          |          |          | `2022 - NeurIPS`   |
-| Neural Net    | Nonstationary Tr.🧑‍🔧[^25] |    ✅     |          |          |          |          | `2022 - NeurIPS`   |
-| Neural Net    | FiLM🧑‍🔧[^22]              |    ✅     |          |          |          |          | `2022 - NeurIPS`   |
-| Neural Net    | RevIN_SCINet🧑‍🔧[^31]      |    ✅     |          |          |          |          | `2022 - ICLR`      |
-| Neural Net    | Pyraformer🧑‍🔧[^26]        |    ✅     |          |          |          |          | `2022 - ICLR`      |
-| Neural Net    | Raindrop[^5]                |          |          |    ✅     |          |          | `2022 - ICLR`      |
-| Neural Net    | FEDformer🧑‍🔧[^20]         |    ✅     |          |          |          |          | `2022 - ICML`      |
-| Neural Net    | Autoformer🧑‍🔧[^15]        |    ✅     |          |          |          |          | `2021 - NeurIPS`   |
-| Neural Net    | CSDI[^12]                   |    ✅     |    ✅     |          |          |          | `2021 - NeurIPS`   |
-| Neural Net    | Informer🧑‍🔧[^21]          |    ✅     |          |          |          |          | `2021 - AAAI`      |
-| Neural Net    | US-GAN[^10]                 |    ✅     |          |          |          |          | `2021 - AAAI`      |
-| Neural Net    | CRLI[^6]                    |          |          |          |    ✅     |          | `2021 - AAAI`      |
-| Probabilistic | BTTF[^8]                    |          |    ✅     |          |          |          | `2021 - TPAMI`     |
-| Neural Net    | StemGNN🧑‍🔧[^33]           |    ✅     |          |          |          |          | `2020 - NeurIPS`   |
-| Neural Net    | Reformer🧑‍🔧[^32]          |    ✅     |          |          |          |          | `2020 - ICLR`      |
-| Neural Net    | GP-VAE[^11]                 |    ✅     |          |          |          |          | `2020 - AISTATS`   |
-| Neural Net    | VaDER[^7]                   |          |          |          |    ✅     |          | `2019 - GigaSci.`  |
-| Neural Net    | M-RNN[^9]                   |    ✅     |          |          |          |          | `2019 - TBME`      |
-| Neural Net    | BRITS[^3]                   |    ✅     |          |    ✅     |          |          | `2018 - NeurIPS`   |
-| Neural Net    | GRU-D[^4]                   |    ✅     |          |    ✅     |          |          | `2018 - Sci. Rep.` |
-| Neural Net    | TCN🧑‍🔧[^35]               |    ✅     |          |          |          |          | `2018 - arXiv`     |
-| Neural Net    | Transformer🧑‍🔧[^2]        |    ✅     |          |          |          |          | `2017 - NeurIPS`   |
-| Naive         | Lerp                        |    ✅     |          |          |          |          |                    |
-| Naive         | LOCF/NOCB                   |    ✅     |          |          |          |          |                    |
-| Naive         | Mean                        |    ✅     |          |          |          |          |                    |
-| Naive         | Median                      |    ✅     |          |          |          |          |                    |
+| **类型**      | **算法**                                                                                                                                                  | **插补** | **预测** | **分类** | **聚类** | **异常检测** | **年份 - 刊物**        |
+|:--------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------|:------:|:------:|:------:|:------:|:--------:|:-------------------|
+| LLM           | <a href="https://time-series.ai"><img src="https://time-series.ai/static/figs/robot.svg" width="26px"> Time-Series.AI</a>  [^36] |    ✅     |    ✅     |    ✅     |    ✅     |    ✅     | `Later in 2024`    |
+| Neural Net    | TimeMixer[^37]                                                                                                                   |    ✅     |          |          |          |          | `2024 - ICLR`      |
+| Neural Net    | iTransformer🧑‍🔧[^24]                                                                                                                                  |    ✅     |          |          |          |          | `2024 - ICLR`      |
+| Neural Net    | ImputeFormer🧑‍🔧[^34]                                                                                                                                  |    ✅     |          |          |          |          | `2024 - KDD`       |
+| Neural Net    | SAITS[^1]                                                                                                                                               |    ✅     |          |          |          |          | `2023 - ESWA`      |
+| Neural Net    | FreTS🧑‍🔧[^23]                                                                                                                                         |    ✅     |          |          |          |          | `2023 - NeurIPS`   |
+| Neural Net    | Koopa🧑‍🔧[^29]                                                                                                                                         |    ✅     |          |          |          |          | `2023 - NeurIPS`   |
+| Neural Net    | Crossformer🧑‍🔧[^16]                                                                                                                                   |    ✅     |          |          |          |          | `2023 - ICLR`      |
+| Neural Net    | TimesNet[^14]                                                                                                                                           |    ✅     |          |          |          |          | `2023 - ICLR`      |
+| Neural Net    | PatchTST🧑‍🔧[^18]                                                                                                                                      |    ✅     |          |          |          |          | `2023 - ICLR`      |
+| Neural Net    | ETSformer🧑‍🔧[^19]                                                                                                                                     |    ✅     |          |          |          |          | `2023 - ICLR`      |
+| Neural Net    | MICN🧑‍🔧[^27]                                                                                                                                          |    ✅     |          |          |          |          | `2023 - ICLR`      |
+| Neural Net    | DLinear🧑‍🔧[^17]                                                                                                                                       |    ✅     |          |          |          |          | `2023 - AAAI`      |
+| Neural Net    | TiDE🧑‍🔧[^28]                                                                                                                                          |    ✅     |          |          |          |          | `2023 - TMLR`      |
+| Neural Net    | SCINet🧑‍🔧[^30]                                                                                                                                        |    ✅     |          |          |          |          | `2022 - NeurIPS`   |
+| Neural Net    | Nonstationary Tr.🧑‍🔧[^25]                                                                                                                             |    ✅     |          |          |          |          | `2022 - NeurIPS`   |
+| Neural Net    | FiLM🧑‍🔧[^22]                                                                                                                                          |    ✅     |          |          |          |          | `2022 - NeurIPS`   |
+| Neural Net    | RevIN_SCINet🧑‍🔧[^31]                                                                                                                                  |    ✅     |          |          |          |          | `2022 - ICLR`      |
+| Neural Net    | Pyraformer🧑‍🔧[^26]                                                                                                                                    |    ✅     |          |          |          |          | `2022 - ICLR`      |
+| Neural Net    | Raindrop[^5]                                                                                                                                            |          |          |    ✅     |          |          | `2022 - ICLR`      |
+| Neural Net    | FEDformer🧑‍🔧[^20]                                                                                                                                     |    ✅     |          |          |          |          | `2022 - ICML`      |
+| Neural Net    | Autoformer🧑‍🔧[^15]                                                                                                                                    |    ✅     |          |          |          |          | `2021 - NeurIPS`   |
+| Neural Net    | CSDI[^12]                                                                                                                                               |    ✅     |    ✅     |          |          |          | `2021 - NeurIPS`   |
+| Neural Net    | Informer🧑‍🔧[^21]                                                                                                                                      |    ✅     |          |          |          |          | `2021 - AAAI`      |
+| Neural Net    | US-GAN[^10]                                                                                                                                             |    ✅     |          |          |          |          | `2021 - AAAI`      |
+| Neural Net    | CRLI[^6]                                                                                                                                                |          |          |          |    ✅     |          | `2021 - AAAI`      |
+| Probabilistic | BTTF[^8]                                                                                                                                                |          |    ✅     |          |          |          | `2021 - TPAMI`     |
+| Neural Net    | StemGNN🧑‍🔧[^33]                                                                                                                                       |    ✅     |          |          |          |          | `2020 - NeurIPS`   |
+| Neural Net    | Reformer🧑‍🔧[^32]                                                                                                                                      |    ✅     |          |          |          |          | `2020 - ICLR`      |
+| Neural Net    | GP-VAE[^11]                                                                                                                                             |    ✅     |          |          |          |          | `2020 - AISTATS`   |
+| Neural Net    | VaDER[^7]                                                                                                                                               |          |          |          |    ✅     |          | `2019 - GigaSci.`  |
+| Neural Net    | M-RNN[^9]                                                                                                                                               |    ✅     |          |          |          |          | `2019 - TBME`      |
+| Neural Net    | BRITS[^3]                                                                                                                                               |    ✅     |          |    ✅     |          |          | `2018 - NeurIPS`   |
+| Neural Net    | GRU-D[^4]                                                                                                                                               |    ✅     |          |    ✅     |          |          | `2018 - Sci. Rep.` |
+| Neural Net    | TCN🧑‍🔧[^35]                                                                                                                                           |    ✅     |          |          |          |          | `2018 - arXiv`     |
+| Neural Net    | Transformer🧑‍🔧[^2]                                                                                                                                    |    ✅     |          |          |          |          | `2017 - NeurIPS`   |
+| Naive         | Lerp                                                                                                                                                    |    ✅     |          |          |          |          |                    |
+| Naive         | LOCF/NOCB                                                                                                                                               |    ✅     |          |          |          |          |                    |
+| Naive         | Mean                                                                                                                                                    |    ✅     |          |          |          |          |                    |
+| Naive         | Median                                                                                                                                                  |    ✅     |          |          |          |          |                    |
 
 💯 现在贡献你的模型来增加你的研究影响力！PyPOTS的下载量正在迅速增长(**[目前PyPI上总共超过30万次且每日超1000的下载](https://www.pepy.tech/projects/pypots)**),
 你的工作将被社区广泛使用和引用。请参阅[贡献指南](https://github.com/WenjieDu/PyPOTS/blob/main/README_zh.md#-%E8%B4%A1%E7%8C%AE%E5%A3%B0%E6%98%8E)，了解如何将模型包含在PyPOTS中。
@@ -292,7 +293,7 @@ year={2023},
    [pypots/imputation/template](https://github.com/WenjieDu/PyPOTS/tree/main/pypots/imputation/template))快速启动你的开发;
 2. 成为[PyPOTS贡献者](https://github.com/WenjieDu/PyPOTS/graphs/contributors)之一，
    并在[PyPOTS网站](https://pypots.com/about/#volunteer-developers)上被列为志愿开发者；
-3. 在我们发布新版本的[更新日志](https://github.com/WenjieDu/PyPOTS/releases)中被提及；
+3. 在PyPOTS发布新版本的[更新日志](https://github.com/WenjieDu/PyPOTS/releases)中被提及；
 
 你也可以通过为该项目设置星标🌟，帮助更多人关注它。你的星标🌟既是对PyPOTS的认可，也是对PyPOTS发展所做出的重要贡献！
 
@@ -363,3 +364,5 @@ PyPOTS社区是一个开放、透明、友好的社区，让我们共同努力
 [^34]: Nie, T., Qin, G., Mei, Y., & Sun, J. (2024). [ImputeFormer: Low Rankness-Induced Transformers for Generalizable Spatiotemporal Imputation](https://arxiv.org/abs/2312.01728). *KDD 2024*.
 [^35]: Bai, S., Kolter, J. Z., & Koltun, V. (2018). [An empirical evaluation of generic convolutional and recurrent networks for sequence modeling](https://arxiv.org/abs/1803.01271). *arXiv 2018*.
 [^36]: Gungnir项目，世界上第一个时间序列多任务大模型，将很快与大家见面。🚀 数据集存在缺少值且样本长短不一？多任务建模场景困难？都不再是问题，让我们的大模型来帮你解决。我们将在近期开放公测申请 ;-) 关注我们，敬请期待!
+       <a href="https://time-series.ai"><img src="https://time-series.ai/static/figs/robot.svg" width="20px" align="center"> Time-Series.AI</a>
+[^37]: Wang, S., Wu, H., Shi, X., Hu, T., Luo, H., Ma, L., ... & ZHOU, J. (2024). [TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting](https://openreview.net/forum?id=7oLshfEIC2). *ICLR 2024*
diff --git a/docs/examples.rst b/docs/examples.rst
index d7d6a1e2..5101eba8 100644
--- a/docs/examples.rst
+++ b/docs/examples.rst
@@ -29,15 +29,13 @@ You can also find a simple and quick-start tutorial notebook on Google Colab
 
     # Data preprocessing. Tedious, but PyPOTS can help. 🤓
     data = load_specific_dataset('physionet_2012')  # PyPOTS will automatically download and extract it.
-    X = data['X']
-    num_samples = len(X['RecordID'].unique())
-    X = X.drop(['RecordID', 'Time'], axis = 1)
-    X = StandardScaler().fit_transform(X.to_numpy())
-    X = X.reshape(num_samples, 48, -1)
+    X = data['train_X']
+    num_samples = len(X)
+    X = StandardScaler().fit_transform(X.reshape(-1, X.shape[-1])).reshape(X.shape)
     X_ori = X  # keep X_ori for validation
     X = mcar(X, 0.1)  # randomly hold out 10% observed values as ground truth
     dataset = {"X": X}  # X for model input
-    print(X.shape)  # (11988, 48, 37), 11988 samples, 48 time steps, 37 features
+    print(X.shape)  # (7671, 48, 37), 7671 samples, 48 time steps, 37 features
 
     # initialize the model
     saits = SAITS(
@@ -55,7 +53,7 @@ You can also find a simple and quick-start tutorial notebook on Google Colab
         model_saving_strategy="best", # only save the model with the best validation performance
     )
 
-    # train the model. Here I use the whole dataset as the training set, because ground truth is not visible to the model.
+    # train the model. Here I consider the train dataset only, and evaluate on it, because ground truth is not visible to the model.
     saits.fit(dataset)
     # impute the originally-missing values and artificially-missing values
     imputation = saits.impute(dataset)
@@ -64,6 +62,6 @@ You can also find a simple and quick-start tutorial notebook on Google Colab
     mae = calc_mae(imputation, np.nan_to_num(X_ori), indicating_mask)  # calculate mean absolute error on the ground truth (artificially-missing values)
 
     # the best model has been already saved, but you can still manually save it with function save_model() as below
-    saits.save_model(saving_dir="examples/saits",file_name="manually_saved_saits_model")
+    saits.save(saving_path="examples/saits/manually_saved_saits_model")
     # you can load the saved model into a new initialized model
-    saits.load_model("examples/saits/manually_saved_saits_model")
+    saits.load("examples/saits/manually_saved_saits_model.pypots")
diff --git a/docs/index.rst b/docs/index.rst
index d6c76d57..c0204b42 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -133,10 +133,12 @@ The paper references are all listed at the bottom of this readme file.
 +----------------+-----------------------------------------------------------+------+------+------+------+------+-----------------------+
 | Type           | Algorithm                                                 | IMPU | FORE | CLAS | CLUS | ANOD | Year - Venue          |
 +================+===========================================================+======+======+======+======+======+=======================+
-| Neural Net     | ImputeFormer      :cite:`nie2024imputeformer`             |  ✅  |      |      |      |      | ``2024 - KDD``        |
+| Neural Net     | TimeMixer      :cite:`wang2024timemixer`                  |  ✅  |      |      |      |      | ``2024 - ICLR``       |
 +----------------+-----------------------------------------------------------+------+------+------+------+------+-----------------------+
 | Neural Net     | iTransformer🧑‍🔧 :cite:`liu2024itransformer`             |  ✅  |      |      |      |      | ``2024 - ICLR``       |
 +----------------+-----------------------------------------------------------+------+------+------+------+------+-----------------------+
+| Neural Net     | ImputeFormer      :cite:`nie2024imputeformer`             |  ✅  |      |      |      |      | ``2024 - KDD``        |
++----------------+-----------------------------------------------------------+------+------+------+------+------+-----------------------+
 | Neural Net     | SAITS :cite:`du2023SAITS`                                 |  ✅  |      |      |      |      | ``2023 - ESWA``       |
 +----------------+-----------------------------------------------------------+------+------+------+------+------+-----------------------+
 | Neural Net     | FreTS🧑‍🔧 :cite:`yi2023frets`                            |  ✅  |      |      |      |      | ``2023 - NeurIPS``    |
@@ -333,7 +335,7 @@ By committing your code, you'll
    `pypots/imputation/template <https://github.com/WenjieDu/PyPOTS/tree/main/pypots/imputation/template>`_) to quickly start;
 2. become one of `PyPOTS contributors <https://github.com/WenjieDu/PyPOTS/graphs/contributors>`_ and
    be listed as a volunteer developer `on the PyPOTS website <https://pypots.com/about/#volunteer-developers>`_;
-3. get mentioned in our `release notes <https://github.com/WenjieDu/PyPOTS/releases>`_;
+3. get mentioned in PyPOTS `release notes <https://github.com/WenjieDu/PyPOTS/releases>`_;
 
 You can also contribute to PyPOTS by simply staring🌟 this repo to help more people notice it.
 Your star is your recognition to PyPOTS, and it matters!
diff --git a/docs/pypots.imputation.rst b/docs/pypots.imputation.rst
index d994f96a..8af2d63f 100644
--- a/docs/pypots.imputation.rst
+++ b/docs/pypots.imputation.rst
@@ -19,6 +19,15 @@ pypots.imputation.transformer
    :show-inheritance:
    :inherited-members:
 
+pypots.imputation.timemixer
+------------------------------------
+
+.. automodule:: pypots.imputation.timemixer
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :inherited-members:
+
 pypots.imputation.imputeformer
 ------------------------------------
 
diff --git a/pypots/data/load_specific_datasets.py b/pypots/data/load_specific_datasets.py
index 69e79615..50c6c297 100644
--- a/pypots/data/load_specific_datasets.py
+++ b/pypots/data/load_specific_datasets.py
@@ -35,7 +35,7 @@ def list_supported_datasets() -> list:
 
 def load_specific_dataset(dataset_name: str, use_cache: bool = True) -> dict:
     """Load specific datasets supported by PyPOTS.
-    Different from tsdb.load_dataset(), which only produces merely raw data,
+    Different from tsdb.load(), which only produces merely raw data,
     load_specific_dataset here does some preprocessing operations,
     like truncating time series to generate samples with the same length.
 
@@ -45,7 +45,7 @@ def load_specific_dataset(dataset_name: str, use_cache: bool = True) -> dict:
         The name of the dataset to be loaded, which should be supported, i.e. in SUPPORTED_DATASETS.
 
     use_cache :
-        Whether to use cache. This is an argument of tsdb.load_dataset().
+        Whether to use cache. This is an argument of tsdb.load().
 
     Returns
     -------


Test patch:
diff --git a/tests/imputation/timemixer.py b/tests/imputation/timemixer.py
index bf5c72cd..a0735663 100644
--- a/tests/imputation/timemixer.py
+++ b/tests/imputation/timemixer.py
@@ -47,8 +47,8 @@ class TestTimeMixer(unittest.TestCase):
         DATA["n_features"],
         n_layers=2,
         top_k=5,
-        d_model=512,
-        d_ffn=512,
+        d_model=32,
+        d_ffn=32,
         dropout=0.1,
         epochs=EPOCHS,
         saving_path=saving_path,