Repository: aeon-toolkit/aeon (python)

Issue:
[ENH] `Differencer` series transformer
### Describe the feature or idea you want to propose

The previous transformer for finding series difference (i.e. first-order differences, second-order differences etc.) is deprecated as it was overcomplicated and used the old interface.

It would be good to have a simpler version in the new interface.

### Describe your proposed solution

A differencer extending `BaseSeriesTransformer`.

### Describe alternatives you've considered, if relevant

_No response_

### Additional context

_No response_

Pull request description:
[ENH] add a difference transformer to series transformations
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#### Reference Issues/PRs
fixes #1553 

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#### What does this implement/fix? Explain your changes.
This implements a transformer that computes the n-order differences of a time series. 

The input time series is expected to have a NumPy inner-type, and the transformation is performed using numpy.diff(). 

To preserve the original shape of the series along the time axis, the first order element(s) of the output are filled with NaN.



<!--
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#### Does your contribution introduce a new dependency? If yes, which one?
No. It only depends on NumPy.

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optional/soft dependency to keep external dependencies of the core aeon package
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#### Any other comments?
No.

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##### For all contributions
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- [x] The PR title starts with either [ENH], [MNT], [DOC], [BUG], [REF], [DEP] or [GOV] indicating whether the PR topic is related to enhancement, maintenance, documentation, bugs, refactoring, deprecation or governance.

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- [ ] I've added the estimator/function to the online [API documentation](https://www.aeon-toolkit.org/en/latest/api_reference.html).
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Patch:
diff --git a/.all-contributorsrc b/.all-contributorsrc
index c7094563..ce303bd1 100644
--- a/.all-contributorsrc
+++ b/.all-contributorsrc
@@ -2710,6 +2710,16 @@
       "contributions": [
         "doc"
       ]
+    },
+    {
+      "login": "TinaJin0228",
+      "name": "Tina Jin",
+      "avatar_url": "https://avatars.githubusercontent.com/TinaJin0228",
+      "profile": "https://github.com/TinaJin0228",
+      "contributions": [
+        "code",
+        "doc"
+      ]
     }
   ],
   "commitType": "docs"
diff --git a/aeon/benchmarking/metrics/anomaly_detection/_continuous.py b/aeon/benchmarking/metrics/anomaly_detection/_continuous.py
index ce84ee33..ade8c102 100644
--- a/aeon/benchmarking/metrics/anomaly_detection/_continuous.py
+++ b/aeon/benchmarking/metrics/anomaly_detection/_continuous.py
@@ -18,12 +18,16 @@ import numpy as np
 from sklearn.metrics import auc, f1_score, precision_recall_curve
 from sklearn.metrics import roc_auc_score as _roc_auc_score
 
+from aeon.benchmarking.metrics.anomaly_detection._range_metrics import (
+    range_f_score,
+    range_precision,
+    range_recall,
+)
 from aeon.benchmarking.metrics.anomaly_detection._util import check_y
 from aeon.benchmarking.metrics.anomaly_detection.thresholding import (
     top_k_points_threshold,
     top_k_ranges_threshold,
 )
-from aeon.utils.validation._dependencies import _check_soft_dependencies
 
 
 def roc_auc_score(y_true: np.ndarray, y_score: np.ndarray) -> float:
@@ -142,9 +146,8 @@ def f_score_at_k_ranges(
     finding a threshold on the scoring that produces at least k anomalous ranges. If `k`
     is not specified, the number of anomalies within the ground truth is used as `k`.
 
-    This implementation uses the community package
-    `prts <https://pypi.org/project/prts/>`_ as a soft-dependency to compute the
-    range-based F-score.
+    This implementation uses the aeon's implementation from `_range_metrics.py` to
+    compute the range-based F-score.
 
     Parameters
     ----------
@@ -167,12 +170,6 @@ def f_score_at_k_ranges(
     aeon.benchmarking.metrics.anomaly_detection.thresholding.top_k_ranges_threshold
         Function used to find the threshold.
     """
-    _check_soft_dependencies(
-        "prts", obj="f_score_at_k_ranges", suppress_import_stdout=True
-    )
-
-    from prts import ts_fscore
-
     y_true, y_pred = check_y(y_true, y_score, force_y_pred_continuous=True)
     if np.unique(y_score).shape[0] == 1:
         warnings.warn(
@@ -183,7 +180,8 @@ def f_score_at_k_ranges(
 
     threshold = top_k_ranges_threshold(y_true, y_score, k)
     y_pred = y_score >= threshold
-    return ts_fscore(y_true, y_pred, p_alpha=1, r_alpha=1, cardinality="reciprocal")
+    # Corrected p_alpha to 1 to match original prts behavior expectation
+    return range_f_score(y_true, y_pred, p_alpha=1, r_alpha=1, cardinality="reciprocal")
 
 
 def rp_rr_auc_score(
@@ -199,8 +197,6 @@ def rp_rr_auc_score(
 
     Computes the area under the precision recall curve when using the range-based
     precision and range-based recall metric introduced by Tatbul et al. at NeurIPS 2018
-    [1]_. This implementation uses the community package
-    `prts <https://pypi.org/project/prts/>`_ as a soft-dependency.
 
     This metric only considers the top-k predicted anomaly ranges within the scoring by
     finding a threshold on the scoring that produces at least k anomalous ranges. If `k`
@@ -242,12 +238,6 @@ def rp_rr_auc_score(
        1920–30. 2018.
        http://papers.nips.cc/paper/7462-precision-and-recall-for-time-series.pdf.
     """
-    _check_soft_dependencies(
-        "prts", obj="f_score_at_k_ranges", suppress_import_stdout=True
-    )
-
-    from prts import ts_precision, ts_recall
-
     y_true, y_pred = check_y(y_true, y_score, force_y_pred_continuous=True)
     if np.unique(y_score).shape[0] == 1:
         warnings.warn(
@@ -279,10 +269,10 @@ def rp_rr_auc_score(
     precisions = np.zeros_like(thresholds)
     for i, threshold in enumerate(thresholds):
         y_pred = (y_score >= threshold).astype(np.int64)
-        recalls[i] = ts_recall(
+        recalls[i] = range_recall(
             y_true, y_pred, alpha=r_alpha, cardinality=cardinality, bias=bias
         )
-        precisions[i] = ts_precision(
+        precisions[i] = range_precision(
             y_true, y_pred, alpha=p_alpha, cardinality=cardinality, bias=bias
         )
     # first sort by recall, then by precision to break ties
diff --git a/aeon/benchmarking/metrics/anomaly_detection/_range_metrics.py b/aeon/benchmarking/metrics/anomaly_detection/_range_metrics.py
index 7143100c..6308cd9b 100644
--- a/aeon/benchmarking/metrics/anomaly_detection/_range_metrics.py
+++ b/aeon/benchmarking/metrics/anomaly_detection/_range_metrics.py
@@ -30,10 +30,6 @@ def range_precision(
     predicted continuous anomaly range the overlap size, position, and cardinality is
     considered. For more details, please refer to the paper [1]_.
 
-    The `alpha` parameter for the existence reward was removed. Because precision
-    emphasizes prediction quality, there is no need for an existence reward and this
-    value should always be set to 0.
-
     Parameters
     ----------
     y_true : np.ndarray
@@ -41,7 +37,7 @@ def range_precision(
     y_pred : np.ndarray
         Anomaly scores for each point of the time series of shape (n_instances,).
     alpha : float
-        DEPRECATED. Default is 0 = no existence reward.
+        Default is 0 = no existence reward.
     cardinality : {'reciprocal', 'one', 'udf_gamma'}
         Cardinality type.
     bias : {'flat', 'front', 'middle', 'back'}
@@ -60,13 +56,6 @@ def range_precision(
        1920–30. 2018.
        http://papers.nips.cc/paper/7462-precision-and-recall-for-time-series.pdf.
     """
-    if alpha != 0:
-        warnings.warn(
-            "The alpha parameter should not be used in range precision. This "
-            "parameter is removed in 1.3.0.",
-            stacklevel=2,
-            category=FutureWarning,
-        )
     y_true, y_pred = check_y(y_true, y_pred, force_y_pred_continuous=False)
     if np.unique(y_pred).shape[0] == 1:
         warnings.warn(
@@ -160,9 +149,6 @@ def range_f_score(
     its optimal value at 1 and its worst value at 0. This implementation uses the
     range-based precision and range-based recall as basis.
 
-    The `p_alpha` parameter for the potential existance reward in the calculation of
-    range-based precision was removed. `p_alpha` should always be set to 0, anyway.
-
     Parameters
     ----------
     y_true : np.ndarray
@@ -173,7 +159,7 @@ def range_f_score(
         F-score beta determines the weight of recall in the combined score.
         beta < 1 lends more weight to precision, while beta > 1 favors recall.
     p_alpha : float
-        DEPRECATED. Default is 0 = no existence reward for precision.
+        Default is 0 = no existence reward for precision.
     r_alpha : float
         Weight of the existence reward. If 0: no existence reward, if 1: only
         existence reward.
@@ -197,14 +183,6 @@ def range_f_score(
        1920–30. 2018.
        http://papers.nips.cc/paper/7462-precision-and-recall-for-time-series.pdf.
     """
-    if p_alpha != 0:
-        warnings.warn(
-            "The p_alpha parameter should not be used. This parameter is removed "
-            "in 1.3.0.",
-            stacklevel=2,
-            category=FutureWarning,
-        )
-
     y_true, y_pred = check_y(y_true, y_pred, force_y_pred_continuous=False)
     if np.unique(y_pred).shape[0] == 1:
         warnings.warn(
@@ -216,7 +194,9 @@ def range_f_score(
     y_pred_ranges = _binary_to_ranges(y_pred)
     y_true_ranges = _binary_to_ranges(y_true)
 
-    precision = _ts_precision(y_pred_ranges, y_true_ranges, cardinality, p_bias)
+    precision = _ts_precision(
+        y_pred_ranges, y_true_ranges, cardinality, p_bias, p_alpha
+    )
     recall = _ts_recall(y_pred_ranges, y_true_ranges, cardinality, r_bias, r_alpha)
 
     if precision + recall > 0:
diff --git a/aeon/benchmarking/metrics/anomaly_detection/_range_ts_metrics.py b/aeon/benchmarking/metrics/anomaly_detection/_range_ts_metrics.py
index 12ad2c0e..1791d367 100644
--- a/aeon/benchmarking/metrics/anomaly_detection/_range_ts_metrics.py
+++ b/aeon/benchmarking/metrics/anomaly_detection/_range_ts_metrics.py
@@ -233,7 +233,7 @@ def _binary_to_ranges(binary_sequence):
     "Please use range_precision instead.",
     category=FutureWarning,
 )
-def ts_precision(y_pred, y_real, gamma="one", bias_type="flat"):
+def ts_precision(y_pred, y_real, gamma="one", bias_type="flat", alpha=0.0):
     """
     Calculate Precision for time series anomaly detection.
 
@@ -317,10 +317,12 @@ def ts_precision(y_pred, y_real, gamma="one", bias_type="flat"):
     flat_y_pred = _flatten_ranges(y_pred_ranges)
     flat_y_real = _flatten_ranges(y_real_ranges)
 
-    return _ts_precision(flat_y_pred, flat_y_real, gamma, bias_type)
+    return _ts_precision(flat_y_pred, flat_y_real, gamma, bias_type, alpha=alpha)
 
 
-def _ts_precision(y_pred_ranges, y_real_ranges, gamma="one", bias_type="flat"):
+def _ts_precision(
+    y_pred_ranges, y_real_ranges, gamma="one", bias_type="flat", alpha=0.0
+):
     """
     Implement range-based precision for time series anomaly detection.
 
@@ -371,13 +373,20 @@ def _ts_precision(y_pred_ranges, y_real_ranges, gamma="one", bias_type="flat"):
         overlap_reward = _calculate_overlap_reward_precision(
             pred_range, overlap_set, bias_type
         )
+
+        existence_reward = 1.0 if overlap_set else 0.0
         gamma_value = _gamma_select(cardinality, gamma)
-        total_overlap_reward += gamma_value * overlap_reward
+
+        score_i = alpha * existence_reward + (1 - alpha) * (
+            gamma_value * overlap_reward
+        )
+
+        total_overlap_reward += score_i
+
         total_cardinality += 1
 
-    precision = (
-        total_overlap_reward / total_cardinality if total_cardinality > 0 else 0.0
-    )
+    precision = total_overlap_reward / total_cardinality if total_cardinality else 0.0
+
     return precision
 
 
@@ -609,7 +618,7 @@ def ts_fscore(
        Processing Systems (NeurIPS 2018), Montréal, Canada.
        http://papers.nips.cc/paper/7462-precision-and-recall-for-time-series.pdf
     """
-    precision = ts_precision(y_pred, y_real, gamma, p_bias)
+    precision = ts_precision(y_pred, y_real, gamma, p_bias, alpha=p_alpha)
     recall = ts_recall(y_pred, y_real, gamma, r_bias, r_alpha)
 
     if precision + recall > 0:
diff --git a/aeon/forecasting/__init__.py b/aeon/forecasting/__init__.py
index de203a0b..7a331f69 100644
--- a/aeon/forecasting/__init__.py
+++ b/aeon/forecasting/__init__.py
@@ -1,13 +1,13 @@
 """Forecasters."""
 
 __all__ = [
-    "DummyForecaster",
+    "NaiveForecaster",
     "BaseForecaster",
     "RegressionForecaster",
     "ETSForecaster",
 ]
 
-from aeon.forecasting._dummy import DummyForecaster
 from aeon.forecasting._ets import ETSForecaster
+from aeon.forecasting._naive import NaiveForecaster
 from aeon.forecasting._regression import RegressionForecaster
 from aeon.forecasting.base import BaseForecaster
diff --git a/aeon/forecasting/_dummy.py b/aeon/forecasting/_naive.py
similarity index 60%
rename from aeon/forecasting/_dummy.py
rename to aeon/forecasting/_naive.py
index 7525b6cc..8b7deede 100644
--- a/aeon/forecasting/_dummy.py
+++ b/aeon/forecasting/_naive.py
@@ -1,24 +1,24 @@
-"""DummyForecaster always predicts the last value seen in training."""
+"""Naive Forecaster."""
 
 from aeon.forecasting.base import BaseForecaster
 
 
-class DummyForecaster(BaseForecaster):
-    """Dummy forecaster always predicts the last value seen in training."""
+class NaiveForecaster(BaseForecaster):
+    """Naive forecaster that always predicts the last value seen in training."""
 
     def __init__(self):
-        """Initialize DummyForecaster."""
+        """Initialize NaiveForecaster."""
         self.last_value_ = None
         super().__init__(horizon=1, axis=1)
 
     def _fit(self, y, exog=None):
-        """Fit dummy forecaster."""
+        """Fit Naive forecaster."""
         y = y.squeeze()
         self.last_value_ = y[-1]
         return self
 
     def _predict(self, y=None, exog=None):
-        """Predict using dummy forecaster."""
+        """Predict using Naive forecaster."""
         return self.last_value_
 
     def _forecast(self, y, exog=None):
diff --git a/aeon/forecasting/base.py b/aeon/forecasting/base.py
index cf2db8d8..23950fa0 100644
--- a/aeon/forecasting/base.py
+++ b/aeon/forecasting/base.py
@@ -4,6 +4,9 @@ A simplified first base class for forecasting models.
 
 """
 
+__maintainer__ = ["TonyBagnall"]
+__all__ = ["BaseForecaster"]
+
 from abc import abstractmethod
 
 import numpy as np
diff --git a/aeon/segmentation/_ggs.py b/aeon/segmentation/_ggs.py
index 0a1bb615..85b08d27 100644
--- a/aeon/segmentation/_ggs.py
+++ b/aeon/segmentation/_ggs.py
@@ -503,12 +503,10 @@ class GreedyGaussianSegmenter(BaseSegmenter):
             dimension of X. The numerical values represent distinct segments
             labels for each of the data points.
         """
-        self.change_points_ = self.ggs.find_change_points(X)
+        change_points_ = self.ggs.find_change_points(X)
 
         labels = np.zeros(X.shape[0], dtype=np.int32)
-        for i, (start, stop) in enumerate(
-            zip(self.change_points_[:-1], self.change_points_[1:])
-        ):
+        for i, (start, stop) in enumerate(zip(change_points_[:-1], change_points_[1:])):
             labels[start:stop] = i
         return labels
 
diff --git a/aeon/segmentation/_igts.py b/aeon/segmentation/_igts.py
index 1bd673b3..dd9aa399 100644
--- a/aeon/segmentation/_igts.py
+++ b/aeon/segmentation/_igts.py
@@ -106,7 +106,7 @@ class _IGTS:
     """
     Information Gain based Temporal Segmentation (GTS).
 
-    GTS is a n unsupervised method for segmenting multivariate time series
+    GTS is an unsupervised method for segmenting multivariate time series
     into non-overlapping segments by locating change points that for which
     the information gain is maximized.
 
@@ -127,18 +127,13 @@ class _IGTS:
 
     Parameters
     ----------
-    k_max: int, default=10
+    k_max : int, default=10
         Maximum number of change points to find. The number of segments is thus k+1.
-    step: : int, default=5
+    step : int, default=5
         Step size, or stride for selecting candidate locations of change points.
         Fox example a `step=5` would produce candidates [0, 5, 10, ...]. Has the same
         meaning as `step` in `range` function.
 
-    Attributes
-    ----------
-    intermediate_results_: list of `ChangePointResult`
-        Intermediate segmentation results for each k value, where k=1, 2, ..., k_max
-
     Notes
     -----
     Based on the work from [1]_.
@@ -366,9 +361,9 @@ class InformationGainSegmenter(BaseSegmenter):
             The numerical values represent distinct segment labels for each of the
             data points.
         """
-        self.change_points_ = self._igts.find_change_points(X)
-        self.intermediate_results_ = self._igts.intermediate_results_
-        return self.to_clusters(self.change_points_[1:-1], X.shape[0])
+        change_points_ = self._igts.find_change_points(X)
+        # self.intermediate_results_ = self._igts.intermediate_results_
+        return self.to_clusters(change_points_[1:-1], X.shape[0])
 
     def __repr__(self) -> str:
         """Return a string representation of the estimator."""
diff --git a/aeon/transformations/series/__init__.py b/aeon/transformations/series/__init__.py
index 8b71ba9f..01bb5faa 100644
--- a/aeon/transformations/series/__init__.py
+++ b/aeon/transformations/series/__init__.py
@@ -21,6 +21,7 @@ __all__ = [
     "SIVSeriesTransformer",
     "PCASeriesTransformer",
     "WarpingSeriesTransformer",
+    "DifferenceTransformer",
 ]
 
 from aeon.transformations.series._acf import (
@@ -32,6 +33,7 @@ from aeon.transformations.series._bkfilter import BKFilter
 from aeon.transformations.series._boxcox import BoxCoxTransformer
 from aeon.transformations.series._clasp import ClaSPTransformer
 from aeon.transformations.series._dft import DFTSeriesTransformer
+from aeon.transformations.series._diff import DifferenceTransformer
 from aeon.transformations.series._dobin import Dobin
 from aeon.transformations.series._exp_smoothing import ExpSmoothingSeriesTransformer
 from aeon.transformations.series._gauss import GaussSeriesTransformer
diff --git a/aeon/transformations/series/_diff.py b/aeon/transformations/series/_diff.py
new file mode 100644
index 00000000..221987b7
--- /dev/null
+++ b/aeon/transformations/series/_diff.py
@@ -0,0 +1,92 @@
+import numpy as np
+
+from aeon.transformations.series.base import BaseSeriesTransformer
+
+__maintainer__ = ["TinaJin0228"]
+__all__ = ["DifferenceTransformer"]
+
+
+class DifferenceTransformer(BaseSeriesTransformer):
+    """
+    Calculates the n-th order difference of a time series.
+
+    Transforms a time series X into a series Y representing the difference
+    calculated `order` times.
+
+    The time series are supposed to be all in rows,
+    with shape (n_channels, n_timepoints)
+
+    - Order 1: Y[t] = X[t] - X[t-1]
+    - Order 2: Y[t] = (X[t] - X[t-1]) - (X[t-1] - X[t-2]) = X[t] - 2*X[t-1] + X[t-2]
+    - ... and so on.
+
+    The transformed series will be shorter than the input series by `order`
+    elements along the time axis.
+
+    Parameters
+    ----------
+    order : int, default=1
+        The order of differencing. Must be a positive integer.
+
+    Notes
+    -----
+    This transformer assumes the input series does not contain NaN values where
+    the difference needs to be computed.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from aeon.transformations.series._diff import DifferenceTransformer
+    >>> X1 = np.array([[1, 3, 2, 5, 4, 7, 6, 9, 8, 10]]) # Shape (1, 10)
+    >>> dt = DifferenceTransformer()
+    >>> Xt1 = dt.fit_transform(X1)
+    >>> print(Xt1) # Shape (1, 9)
+    [[ 2 -1  3 -1  3 -1  3 -1  2]]
+
+    >>> X2 = np.array([[1, 3, 2, 5, 4, 7, 6, 9, 8, 10]]) # Shape (1, 10)
+    >>> dt2 = DifferenceTransformer(order=2)
+    >>> Xt2 = dt2.fit_transform(X2)
+    >>> print(Xt2) # Shape (1, 8)
+    [[-3  4 -4  4 -4  4 -4  3]]
+
+    >>> X3 = np.array([[1, 2, 3, 4, 5], [5, 4, 3, 2, 1]]) # Shape (2, 5)
+    >>> dt = DifferenceTransformer()
+    >>> Xt3 = dt.fit_transform(X3)
+    >>> print(Xt3) # Shape (2, 4)
+    [[ 1  1  1  1]
+     [-1 -1 -1 -1]]
+    """
+
+    _tags = {
+        "capability:multivariate": True,
+        "X_inner_type": "np.ndarray",
+        "fit_is_empty": True,
+    }
+
+    def __init__(self, order=1):
+        self.order = order
+        super().__init__(axis=1)
+
+    def _transform(self, X, y=None):
+        """
+        Perform the n-th order differencing transformation.
+
+        Parameters
+        ----------
+        X : Time series to transform. With shape (n_channels, n_timepoints).
+        y : ignored argument for interface compatibility
+
+        Returns
+        -------
+        Xt : np.ndarray
+        """
+        if not isinstance(self.order, int) or self.order < 1:
+            raise ValueError(
+                f"`order` must be a positive integer, but got {self.order}"
+            )
+
+        diff_X = np.diff(X, n=self.order, axis=1)
+
+        Xt = diff_X
+
+        return Xt
diff --git a/examples/forecasting/forecasting.ipynb b/examples/forecasting/forecasting.ipynb
index 0e0b4ac7..5d98504f 100644
--- a/examples/forecasting/forecasting.ipynb
+++ b/examples/forecasting/forecasting.ipynb
@@ -193,9 +193,9 @@
    "cell_type": "code",
    "source": [
     "# Fit then predict\n",
-    "from aeon.forecasting import DummyForecaster\n",
+    "from aeon.forecasting import NaiveForecaster\n",
     "\n",
-    "d = DummyForecaster()\n",
+    "d = NaiveForecaster()\n",
     "print(d.get_tag(\"y_inner_type\"))\n",
     "d.fit(y)\n",
     "p = d.predict()\n",


Test patch:
diff --git a/aeon/benchmarking/metrics/anomaly_detection/tests/test_ad_metrics.py b/aeon/benchmarking/metrics/anomaly_detection/tests/test_ad_metrics.py
index fd14aa39..e2e5364e 100644
--- a/aeon/benchmarking/metrics/anomaly_detection/tests/test_ad_metrics.py
+++ b/aeon/benchmarking/metrics/anomaly_detection/tests/test_ad_metrics.py
@@ -20,9 +20,8 @@ from aeon.benchmarking.metrics.anomaly_detection._range_metrics import (
     range_recall,
 )
 from aeon.testing.data_generation import make_example_1d_numpy
-from aeon.utils.validation._dependencies import _check_soft_dependencies
 
-pr_metrics = [pr_auc_score]
+pr_metrics = [pr_auc_score, rp_rr_auc_score]
 range_metrics = [
     range_roc_auc_score,
     range_pr_auc_score,
@@ -32,6 +31,7 @@ range_metrics = [
 other_metrics = [
     roc_auc_score,
     f_score_at_k_points,
+    f_score_at_k_ranges,
 ]
 continuous_metrics = [*pr_metrics, *other_metrics, *range_metrics]
 binary_metrics = [
@@ -40,21 +40,6 @@ binary_metrics = [
     range_recall,
 ]
 
-if _check_soft_dependencies("prts", severity="none"):
-    pr_metrics.append(rp_rr_auc_score)
-    other_metrics.extend(
-        [
-            f_score_at_k_ranges,
-            rp_rr_auc_score,
-        ]
-    )
-    continuous_metrics.extend(
-        [
-            rp_rr_auc_score,
-            f_score_at_k_ranges,
-        ]
-    )
-
 metrics = [*pr_metrics, *range_metrics, *other_metrics]
 
 
@@ -148,10 +133,6 @@ def test_edge_cases_pr_metrics(metric):
     assert score <= 0.2, f"{metric.__name__}(y_true, y_inverted)={score} is not <= 0.2"
 
 
-@pytest.mark.skipif(
-    not _check_soft_dependencies("prts", severity="none"),
-    reason="required soft dependency prts not available",
-)
 def test_range_based_f1():
     """Test range-based F1 score."""
     y_pred = np.array([0, 1, 1, 0])
@@ -160,10 +141,6 @@ def test_range_based_f1():
     np.testing.assert_almost_equal(result, 0.66666, decimal=4)
 
 
-@pytest.mark.skipif(
-    not _check_soft_dependencies("prts", severity="none"),
-    reason="required soft dependency prts not available",
-)
 def test_range_based_precision():
     """Test range-based precision."""
     y_pred = np.array([0, 1, 1, 0])
@@ -172,10 +149,6 @@ def test_range_based_precision():
     assert result == 0.5
 
 
-@pytest.mark.skipif(
-    not _check_soft_dependencies("prts", severity="none"),
-    reason="required soft dependency prts not available",
-)
 def test_range_based_recall():
     """Test range-based recall."""
     y_pred = np.array([0, 1, 1, 0])
@@ -184,10 +157,6 @@ def test_range_based_recall():
     assert result == 1
 
 
-@pytest.mark.skipif(
-    not _check_soft_dependencies("prts", severity="none"),
-    reason="required soft dependency prts not available",
-)
 def test_rf1_value_error():
     """Test range-based F1 score raises ValueError on binary predictions."""
     y_pred = np.array([0, 0.2, 0.7, 0])
@@ -211,10 +180,6 @@ def test_pr_curve_auc():
 #     np.testing.assert_almost_equal(result, 0.8333, decimal=4)
 
 
-@pytest.mark.skipif(
-    not _check_soft_dependencies("prts", severity="none"),
-    reason="required soft dependency prts not available",
-)
 def test_range_based_p_range_based_r_curve_auc():
     """Test range-based precision-recall curve AUC."""
     y_pred = np.array([0, 0.1, 1.0, 0.5, 0.1, 0])
@@ -223,10 +188,6 @@ def test_range_based_p_range_based_r_curve_auc():
     np.testing.assert_almost_equal(result, 0.9792, decimal=4)
 
 
-@pytest.mark.skipif(
-    not _check_soft_dependencies("prts", severity="none"),
-    reason="required soft dependency prts not available",
-)
 def test_range_based_p_range_based_r_auc_perfect_hit():
     """Test range-based precision-recall curve AUC with perfect hit."""
     y_pred = np.array([0, 0, 0.5, 0.5, 0, 0])
@@ -235,10 +196,6 @@ def test_range_based_p_range_based_r_auc_perfect_hit():
     np.testing.assert_almost_equal(result, 1.0000, decimal=4)
 
 
-@pytest.mark.skipif(
-    not _check_soft_dependencies("prts", severity="none"),
-    reason="required soft dependency prts not available",
-)
 def test_f_score_at_k_ranges():
     """Test range-based F1 score at k ranges."""
     y_pred = np.array([0.4, 0.1, 1.0, 0.5, 0.1, 0, 0.4, 0.5])
diff --git a/aeon/forecasting/tests/test_base.py b/aeon/forecasting/tests/test_base.py
index 22c15901..bd4e26a6 100644
--- a/aeon/forecasting/tests/test_base.py
+++ b/aeon/forecasting/tests/test_base.py
@@ -4,12 +4,12 @@ import numpy as np
 import pandas as pd
 import pytest
 
-from aeon.forecasting import DummyForecaster
+from aeon.forecasting import NaiveForecaster
 
 
 def test_base_forecaster():
     """Test base forecaster functionality."""
-    f = DummyForecaster()
+    f = NaiveForecaster()
     y = np.random.rand(50)
     f.fit(y)
     p1 = f.predict()
@@ -26,7 +26,7 @@ def test_base_forecaster():
 
 def test_convert_y():
     """Test y conversion in forecasting base."""
-    f = DummyForecaster()
+    f = NaiveForecaster()
     y = np.random.rand(50)
     with pytest.raises(ValueError, match="Input axis should be 0 or 1"):
         f._convert_y(y, axis=2)
diff --git a/aeon/segmentation/tests/test_igts.py b/aeon/segmentation/tests/test_igts.py
index 7103dca3..0aaa0f2d 100644
--- a/aeon/segmentation/tests/test_igts.py
+++ b/aeon/segmentation/tests/test_igts.py
@@ -51,5 +51,3 @@ def test_InformationGainSegmenter(multivariate_mean_shift):
             [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3], dtype=int
         ),
     )
-    assert igts.change_points_ == [0, 5, 10, 15, 20]
-    assert len(igts.intermediate_results_) == 3
diff --git a/aeon/testing/testing_config.py b/aeon/testing/testing_config.py
index 3e47dcf0..fe7f8d00 100644
--- a/aeon/testing/testing_config.py
+++ b/aeon/testing/testing_config.py
@@ -55,8 +55,6 @@ EXCLUDED_TESTS = {
     "LeftSTAMPi": ["check_anomaly_detector_output"],
     # missed in legacy testing, changes state in predict/transform
     "FLUSSSegmenter": ["check_non_state_changing_method"],
-    "InformationGainSegmenter": ["check_non_state_changing_method"],
-    "GreedyGaussianSegmenter": ["check_non_state_changing_method"],
     "ClaSPSegmenter": ["check_non_state_changing_method"],
     "HMMSegmenter": ["check_non_state_changing_method"],
     "RSTSF": ["check_non_state_changing_method"],
diff --git a/aeon/transformations/series/tests/test_diff.py b/aeon/transformations/series/tests/test_diff.py
new file mode 100644
index 00000000..9f54fccf
--- /dev/null
+++ b/aeon/transformations/series/tests/test_diff.py
@@ -0,0 +1,37 @@
+"""Tests for Difference transformation."""
+
+import numpy as np
+
+from aeon.transformations.series._diff import DifferenceTransformer
+
+
+def test_diff():
+    """Tests basic first and second order differencing."""
+    X = np.array([[1.0, 4.0, 9.0, 16.0, 25.0, 36.0]])
+
+    dt1 = DifferenceTransformer(order=1)
+    Xt1 = dt1.fit_transform(X)
+    expected1 = np.array([[3.0, 5.0, 7.0, 9.0, 11.0]])
+
+    np.testing.assert_allclose(
+        Xt1, expected1, equal_nan=True, err_msg="Value mismatch for order 1"
+    )
+
+    dt2 = DifferenceTransformer(order=2)
+    Xt2 = dt2.fit_transform(X)
+    expected2 = np.array([[2.0, 2.0, 2.0, 2.0]])
+
+    np.testing.assert_allclose(
+        Xt2, expected2, equal_nan=True, err_msg="Value mismatch for order 2"
+    )
+
+    Y = np.array([[1, 2, 3, 4], [5, 3, 1, 8]])
+
+    Yt1 = dt1.fit_transform(Y)
+    expected3 = np.array([[1, 1, 1], [-2, -2, 7]])
+    np.testing.assert_allclose(
+        Yt1,
+        expected3,
+        equal_nan=True,
+        err_msg="Value mismatch for order 1,multivariate",
+    )