From 4619eadc0c082af399f6c59b65a4beca7fe0d66b Mon Sep 17 00:00:00 2001
From: MatthewMiddlehurst <pfm15hbu@gmail.com>
Date: Sun, 10 Aug 2025 20:38:39 +0100
Subject: [PATCH 1/7] banish update and inverse transform

---
 aeon/transformations/base.py                  |  90 +++++++++++++-
 .../collection/_broadcaster.py                |   3 +-
 aeon/transformations/collection/base.py       |  94 +--------------
 .../collection/compose/_identity.py           |   4 +-
 .../collection/dictionary_based/_borf.py      |   1 -
 aeon/transformations/series/_boxcox.py        |   4 +-
 aeon/transformations/series/_log.py           |   4 +-
 aeon/transformations/series/_scaled_logit.py  |   4 +-
 aeon/transformations/series/base.py           | 111 ++----------------
 .../series/compose/_identity.py               |   4 +-
 10 files changed, 119 insertions(+), 200 deletions(-)

diff --git a/aeon/transformations/base.py b/aeon/transformations/base.py
index f0ae37d008..fe2c8a5199 100644
--- a/aeon/transformations/base.py
+++ b/aeon/transformations/base.py
@@ -3,12 +3,15 @@
 __maintainer__ = ["MatthewMiddlehurst", "TonyBagnall"]
 __all__ = ["BaseTransformer"]
 
-from abc import abstractmethod
+from abc import ABC, abstractmethod
+from typing import final
 
 import numpy as np
 import pandas as pd
 
 from aeon.base import BaseAeonEstimator
+from aeon.transformations.collection import BaseCollectionTransformer
+from aeon.transformations.series import BaseSeriesTransformer
 
 
 class BaseTransformer(BaseAeonEstimator):
@@ -112,3 +115,88 @@ def _check_y(self, y, n_cases=None):
                     f"Mismatch in number of cases. Number in X = {n_cases} nos in y = "
                     f"{n_labels}"
                 )
+
+
+class InverseTransformerMixin(ABC):
+    """Mixin for transformers that support inverse transformation."""
+
+    _tags = {
+        "capability:inverse_transform": True,
+    }
+
+    @final
+    def inverse_transform(self, X, y=None, axis=1):
+        """Inverse transform X and return an inverse transformed version.
+
+        Currently it is assumed that only transformers with tags
+             "input_data_type"="Series", "output_data_type"="Series",
+        can have an inverse_transform.
+
+        State required:
+             Requires state to be "fitted".
+
+        Accesses in self:
+         _is_fitted : must be True
+         fitted model attributes (ending in "_") : accessed by _inverse_transform
+
+        Parameters
+        ----------
+        X : Series or Collection, any supported type
+            Data to fit transform to, of python type as follows:
+                Series: 2D np.ndarray shape (n_channels, n_timepoints)
+                Collection: 3D np.ndarray shape (n_cases, n_channels, n_timepoints)
+                or list of 2D np.ndarray, case i has shape (n_channels, n_timepoints_i)
+        y : Series, default=None
+            Additional data, e.g., labels for transformation.
+        axis : int, default = 1
+            Axis of time in the input series.
+            If ``axis == 0``, it is assumed each column is a time series and each row is
+            a time point. i.e. the shape of the data is ``(n_timepoints,
+            n_channels)``.
+            ``axis == 1`` indicates the time series are in rows, i.e. the shape of
+            the data is ``(n_channels, n_timepoints)`.``axis is None`` indicates
+            that the axis of X is the same as ``self.axis``.
+
+            Only relevant for ``aeon.transformations.series`` transformers.
+
+        Returns
+        -------
+        inverse transformed version of X
+            of the same type as X
+        """
+        # check whether is fitted
+        self._check_is_fitted()
+
+        # input check and conversion for X/y
+        if isinstance(self, BaseCollectionTransformer):
+            X_inner = self._preprocess_collection(X, store_metadata=False)
+            Xt = self._inverse_transform(X=X_inner, y=y)
+            return Xt
+        elif isinstance(self, BaseSeriesTransformer):
+            self._check_is_fitted()
+            X = self._preprocess_series(X, axis=axis, store_metadata=False)
+            Xt = self._inverse_transform(X=X, y=y)
+            return self._postprocess_series(Xt, axis=axis)
+
+    @abstractmethod
+    def _inverse_transform(self, X, y=None):
+        """Inverse transform X and return an inverse transformed version.
+
+        private _inverse_transform containing core logic, called from inverse_transform.
+
+        Parameters
+        ----------
+        X : Series or Collection, any supported type
+            Data to fit transform to, of python type as follows:
+                Series: 2D np.ndarray shape (n_channels, n_timepoints)
+                Collection: 3D np.ndarray shape (n_cases, n_channels, n_timepoints)
+                or list of 2D np.ndarray, case i has shape (n_channels, n_timepoints_i)
+        y : Series, default=None
+            Additional data, e.g., labels for transformation.
+
+        Returns
+        -------
+        inverse transformed version of X
+            of the same type as X.
+        """
+        ...
diff --git a/aeon/transformations/collection/_broadcaster.py b/aeon/transformations/collection/_broadcaster.py
index 742e24d2c1..c422fdc6d8 100644
--- a/aeon/transformations/collection/_broadcaster.py
+++ b/aeon/transformations/collection/_broadcaster.py
@@ -5,12 +5,13 @@
 
 import numpy as np
 
+from aeon.transformations.base import InverseTransformerMixin
 from aeon.transformations.collection.base import BaseCollectionTransformer
 from aeon.transformations.series.base import BaseSeriesTransformer
 from aeon.utils.validation import get_n_cases
 
 
-class SeriesToCollectionBroadcaster(BaseCollectionTransformer):
+class SeriesToCollectionBroadcaster(BaseCollectionTransformer, InverseTransformerMixin):
     """Broadcast a ``BaseSeriesTransformer`` over a collection of time series.
 
     Uses the ``BaseSeriesTransformer`` passed in the constructor.  If the
diff --git a/aeon/transformations/collection/base.py b/aeon/transformations/collection/base.py
index 4463f4a4ee..f382683217 100644
--- a/aeon/transformations/collection/base.py
+++ b/aeon/transformations/collection/base.py
@@ -211,83 +211,21 @@ def fit_transform(self, X, y=None):
         self.is_fitted = True
         return Xt
 
-    @final
-    def inverse_transform(self, X, y=None):
-        """Inverse transform X and return an inverse transformed version.
-
-        Currently it is assumed that only transformers with tags
-             "input_data_type"="Series", "output_data_type"="Series",
-        can have an inverse_transform.
-
-        State required:
-             Requires state to be "fitted".
-
-        Accesses in self:
-         _is_fitted : must be True
-         fitted model attributes (ending in "_") : accessed by _inverse_transform
-
-        Parameters
-        ----------
-        X : np.ndarray or list
-            Data to fit transform to, of valid collection type. Input data,
-            any number of channels, equal length series of shape ``(
-            n_cases, n_channels, n_timepoints)`` or list of numpy arrays (number
-            of channels, series length) of shape ``[n_cases]``, 2D np.array
-            ``(n_channels, n_timepoints_i)``, where ``n_timepoints_i`` is length of
-            series ``i``. Other types are allowed and converted into one of the above.
-
-            Different estimators have different capabilities to handle different
-            types of input. If ``self.get_tag("capability:multivariate")`` is False,
-            they cannot handle multivariate series. If ``self.get_tag(
-            "capability:unequal_length")`` is False, they cannot handle unequal
-            length input. In both situations, a ``ValueError`` is raised if X has a
-            characteristic that the estimator does not have the capability to handle.
-        y : np.ndarray, default=None
-            1D np.array of float or str, of shape ``(n_cases)`` - class labels
-            (ground truth) for fitting indices corresponding to instance indices in X.
-            If None, no labels are used in fitting.
-
-        Returns
-        -------
-        inverse transformed version of X
-            of the same type as X
-        """
-        if not self.get_tag("capability:inverse_transform"):
-            raise NotImplementedError(
-                f"{type(self)} does not implement inverse_transform"
-            )
-
-        # check whether is fitted
-        self._check_is_fitted()
-
-        # input check and conversion for X/y
-        X_inner = self._preprocess_collection(X, store_metadata=False)
-        y_inner = y
-
-        Xt = self._inverse_transform(X=X_inner, y=y_inner)
-
-        return Xt
-
     def _fit(self, X, y=None):
         """Fit transformer to X and y.
 
         private _fit containing the core logic, called from fit
 
+        private _transform containing the core logic, called from transform
+
         Parameters
         ----------
         X : Input data
             Data to fit transform to, of valid collection type.
         y : Target variable, default=None
             Additional data, e.g., labels for transformation
-
-        Returns
-        -------
-        self: a fitted instance of the estimator
-
-        See extension_templates/transformer.py for implementation details.
         """
-        # default fit is "no fitting happens"
-        return self
+        ...
 
     @abstractmethod
     def _transform(self, X, y=None):
@@ -315,6 +253,9 @@ def _fit_transform(self, X, y=None):
 
         private _fit_transform containing the core logic, called from fit_transform.
 
+        Non-optimized default implementation; override when a better
+        method is possible for a given algorithm.
+
         Parameters
         ----------
         X : Input data
@@ -326,29 +267,6 @@ def _fit_transform(self, X, y=None):
         -------
         transformed version of X.
         """
-        # Non-optimized default implementation; override when a better
-        # method is possible for a given algorithm.
         if not self.get_tag("fit_is_empty"):
             self._fit(X, y)
         return self._transform(X, y)
-
-    def _inverse_transform(self, X, y=None):
-        """Inverse transform X and return an inverse transformed version.
-
-        private _inverse_transform containing core logic, called from inverse_transform.
-
-        Parameters
-        ----------
-        X : Input data
-            Data to fit transform to, of valid collection type.
-        y : Target variable, default=None
-            Additional data, e.g., labels for transformation
-
-        Returns
-        -------
-        inverse transformed version of X
-            of the same type as X.
-        """
-        raise NotImplementedError(
-            f"{self.__class__.__name__} does not support inverse_transform"
-        )
diff --git a/aeon/transformations/collection/compose/_identity.py b/aeon/transformations/collection/compose/_identity.py
index a359255242..e197fd2fa6 100644
--- a/aeon/transformations/collection/compose/_identity.py
+++ b/aeon/transformations/collection/compose/_identity.py
@@ -1,16 +1,16 @@
 """Identity transformer."""
 
+from aeon.transformations.base import InverseTransformerMixin
 from aeon.transformations.collection import BaseCollectionTransformer
 from aeon.utils.data_types import COLLECTIONS_DATA_TYPES
 
 
-class CollectionId(BaseCollectionTransformer):
+class CollectionId(BaseCollectionTransformer, InverseTransformerMixin):
     """Identity transformer, returns data unchanged in transform/inverse_transform."""
 
     _tags = {
         "X_inner_type": COLLECTIONS_DATA_TYPES,
         "fit_is_empty": True,
-        "capability:inverse_transform": True,
         "capability:multivariate": True,
         "capability:unequal_length": True,
         "capability:missing_values": True,
diff --git a/aeon/transformations/collection/dictionary_based/_borf.py b/aeon/transformations/collection/dictionary_based/_borf.py
index 8b67d084c7..029a9c5780 100644
--- a/aeon/transformations/collection/dictionary_based/_borf.py
+++ b/aeon/transformations/collection/dictionary_based/_borf.py
@@ -100,7 +100,6 @@ class BORF(BaseCollectionTransformer):
 
     _tags = {
         "X_inner_type": "numpy3D",
-        "capability:inverse_transform": False,
         "capability:missing_values": True,
         "capability:multivariate": True,
         "capability:multithreading": True,
diff --git a/aeon/transformations/series/_boxcox.py b/aeon/transformations/series/_boxcox.py
index 37f0f14550..01fa1195cc 100644
--- a/aeon/transformations/series/_boxcox.py
+++ b/aeon/transformations/series/_boxcox.py
@@ -8,6 +8,7 @@
 from scipy.special import boxcox, inv_boxcox
 from scipy.stats import boxcox_llf, distributions, variation
 
+from aeon.transformations.base import InverseTransformerMixin
 from aeon.transformations.series.base import BaseSeriesTransformer
 from aeon.utils.validation import is_int
 
@@ -39,7 +40,7 @@ def _calc_uniform_order_statistic_medians(n):
     return v
 
 
-class BoxCoxTransformer(BaseSeriesTransformer):
+class BoxCoxTransformer(BaseSeriesTransformer, InverseTransformerMixin):
     r"""Box-Cox power transform.
 
     Box-Cox transformation is a power transformation that is used to
@@ -107,7 +108,6 @@ class BoxCoxTransformer(BaseSeriesTransformer):
         "X_inner_type": "np.ndarray",
         "fit_is_empty": False,
         "capability:multivariate": False,
-        "capability:inverse_transform": True,
     }
 
     def __init__(self, bounds=None, method="mle", sp=None):
diff --git a/aeon/transformations/series/_log.py b/aeon/transformations/series/_log.py
index 45d72664f5..b82f104430 100644
--- a/aeon/transformations/series/_log.py
+++ b/aeon/transformations/series/_log.py
@@ -5,10 +5,11 @@
 
 import numpy as np
 
+from aeon.transformations.base import InverseTransformerMixin
 from aeon.transformations.series.base import BaseSeriesTransformer
 
 
-class LogTransformer(BaseSeriesTransformer):
+class LogTransformer(BaseSeriesTransformer, InverseTransformerMixin):
     """Natural logarithm transformation.
 
     The Natural logarithm transformation can be used to make the data more normally
@@ -41,7 +42,6 @@ class LogTransformer(BaseSeriesTransformer):
         "X_inner_type": "np.ndarray",
         "fit_is_empty": True,
         "capability:multivariate": True,
-        "capability:inverse_transform": True,
     }
 
     def __init__(self, offset=0, scale=1):
diff --git a/aeon/transformations/series/_scaled_logit.py b/aeon/transformations/series/_scaled_logit.py
index e400385f35..e94e659e89 100644
--- a/aeon/transformations/series/_scaled_logit.py
+++ b/aeon/transformations/series/_scaled_logit.py
@@ -8,10 +8,11 @@
 
 import numpy as np
 
+from aeon.transformations.base import InverseTransformerMixin
 from aeon.transformations.series.base import BaseSeriesTransformer
 
 
-class ScaledLogitSeriesTransformer(BaseSeriesTransformer):
+class ScaledLogitSeriesTransformer(BaseSeriesTransformer, InverseTransformerMixin):
     r"""Scaled logit transform or Log transform.
 
     If both lower_bound and upper_bound are not None, a scaled logit transform is
@@ -59,7 +60,6 @@ class ScaledLogitSeriesTransformer(BaseSeriesTransformer):
         "X_inner_type": "np.ndarray",
         "fit_is_empty": True,
         "capability:multivariate": True,
-        "capability:inverse_transform": True,
     }
 
     def __init__(self, lower_bound=None, upper_bound=None):
diff --git a/aeon/transformations/series/base.py b/aeon/transformations/series/base.py
index 3afa1011bc..86fe68684a 100644
--- a/aeon/transformations/series/base.py
+++ b/aeon/transformations/series/base.py
@@ -111,9 +111,8 @@ def transform(self, X, y=None, axis=1):
         if y is not None:
             self._check_y(y)
 
-        # #2768
-        # if not fit_empty:
-        #     self._check_shape(X)
+        if not fit_empty:
+            self._check_shape(X)
 
         Xt = self._transform(X, y)
         return self._postprocess_series(Xt, axis=axis)
@@ -165,86 +164,22 @@ def fit_transform(self, X, y=None, axis=1):
         self.is_fitted = True
         return self._postprocess_series(Xt, axis=axis)
 
-    @final
-    def inverse_transform(self, X, y=None, axis=1):
-        """Inverse transform X and return an inverse transformed version.
-
-        State required:
-             Requires state to be "fitted".
-
-        Parameters
-        ----------
-        X : Input data
-            Data to fit transform to, of valid collection type.
-        y : Target variable, default=None
-             Additional data, e.g., labels for transformation
-        axis : int, default = 1
-            Axis of time in the input series.
-            If ``axis == 0``, it is assumed each column is a time series and each row is
-            a time point. i.e. the shape of the data is ``(n_timepoints,
-            n_channels)``.
-            ``axis == 1`` indicates the time series are in rows, i.e. the shape of
-            the data is ``(n_channels, n_timepoints)`.``axis is None`` indicates
-            that the axis of X is the same as ``self.axis``.
-
-        Returns
-        -------
-        inverse transformed version of X
-            of the same type as X
-        """
-        if not self.get_tag("capability:inverse_transform"):
-            raise NotImplementedError(
-                f"{type(self)} does not implement inverse_transform"
-            )
-
-        # check whether is fitted
-        self._check_is_fitted()
-        X = self._preprocess_series(X, axis=axis, store_metadata=False)
-        Xt = self._inverse_transform(X=X, y=y)
-        return self._postprocess_series(Xt, axis=axis)
-
-    @final
-    def update(self, X, y=None, update_params=True, axis=1):
-        """Update transformer with X, optionally y.
-
-        Parameters
-        ----------
-        X : data to update of valid series type.
-        y : Target variable, default=None
-            Additional data, e.g., labels for transformation
-        update_params : bool, default=True
-            whether the model is updated. Yes if true, if false, simply skips call.
-            argument exists for compatibility with forecasting module.
-        axis : int, default=None
-            axis along which to update. If None, uses self.axis.
-
-        Returns
-        -------
-        self : a fitted instance of the estimator
-        """
-        # check whether is fitted
-        self._check_is_fitted()
-        X = self._preprocess_series(X, axis, False)
-        return self._update(X=X, y=y, update_params=update_params)
-
     def _fit(self, X, y=None):
         """Fit transformer to X and y.
 
         private _fit containing the core logic, called from fit
 
+        A default implementation for the fit_is_empty tag. Other transformers
+        should override this.
+
         Parameters
         ----------
         X : Input data
             Data to fit transform to, of valid collection type.
         y : Target variable, default=None
             Additional data, e.g., labels for transformation
-
-        Returns
-        -------
-        self: a fitted instance of the estimator
         """
-        # default fit is "no fitting happens"
-        return self
+        ...
 
     @abstractmethod
     def _transform(self, X, y=None):
@@ -263,6 +198,7 @@ def _transform(self, X, y=None):
         -------
         transformed version of X
         """
+        ...
 
     def _fit_transform(self, X, y=None):
         """Fit to data, then transform it.
@@ -271,6 +207,9 @@ def _fit_transform(self, X, y=None):
 
         private _fit_transform containing the core logic, called from fit_transform.
 
+        Non-optimised default implementation; override when a better
+        method is possible for a given algorithm.
+
         Parameters
         ----------
         X : Input data
@@ -282,36 +221,10 @@ def _fit_transform(self, X, y=None):
         -------
         transformed version of X.
         """
-        # Non-optimized default implementation; override when a better
-        # method is possible for a given algorithm.
-        self._fit(X, y)
+        if not self.get_tag("fit_is_empty"):
+            self._fit(X, y)
         return self._transform(X, y)
 
-    def _inverse_transform(self, X, y=None):
-        """Inverse transform X and return an inverse transformed version.
-
-        private _inverse_transform containing core logic, called from inverse_transform.
-
-        Parameters
-        ----------
-        X : Input data
-            Time series to fit transform to, of valid collection type.
-        y : Target variable, default=None
-            Additional data, e.g., labels for transformation
-
-        Returns
-        -------
-        inverse transformed version of X
-            of the same type as X.
-        """
-        raise NotImplementedError(
-            f"{self.__class__.__name__} does not support inverse_transform"
-        )
-
-    def _update(self, X, y=None, update_params=True):
-        # standard behaviour: no update takes place, new data is ignored
-        return self
-
     def _postprocess_series(self, Xt, axis):
         """Postprocess data Xt to revert to original shape.
 
diff --git a/aeon/transformations/series/compose/_identity.py b/aeon/transformations/series/compose/_identity.py
index d9135fae36..c67d200c19 100644
--- a/aeon/transformations/series/compose/_identity.py
+++ b/aeon/transformations/series/compose/_identity.py
@@ -1,16 +1,16 @@
 """Identity transformer."""
 
+from aeon.transformations.base import InverseTransformerMixin
 from aeon.transformations.series import BaseSeriesTransformer
 from aeon.utils.data_types import VALID_SERIES_INNER_TYPES
 
 
-class SeriesId(BaseSeriesTransformer):
+class SeriesId(BaseSeriesTransformer, InverseTransformerMixin):
     """Identity transformer, returns data unchanged in transform/inverse_transform."""
 
     _tags = {
         "X_inner_type": VALID_SERIES_INNER_TYPES,
         "fit_is_empty": True,
-        "capability:inverse_transform": True,
         "capability:multivariate": True,
         "capability:missing_values": True,
     }

From 889f566424feff4ac753f8bc099cd2a9fcfdc032 Mon Sep 17 00:00:00 2001
From: MatthewMiddlehurst <pfm15hbu@gmail.com>
Date: Sun, 10 Aug 2025 20:39:47 +0100
Subject: [PATCH 2/7] revert

---
 aeon/transformations/series/base.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/aeon/transformations/series/base.py b/aeon/transformations/series/base.py
index 86fe68684a..f89404d1f9 100644
--- a/aeon/transformations/series/base.py
+++ b/aeon/transformations/series/base.py
@@ -111,8 +111,9 @@ def transform(self, X, y=None, axis=1):
         if y is not None:
             self._check_y(y)
 
-        if not fit_empty:
-            self._check_shape(X)
+        # #2768
+        # if not fit_empty:
+        #     self._check_shape(X)
 
         Xt = self._transform(X, y)
         return self._postprocess_series(Xt, axis=axis)

From 798a424111025610643be599c17df5335af24870 Mon Sep 17 00:00:00 2001
From: MatthewMiddlehurst <pfm15hbu@gmail.com>
Date: Wed, 20 Aug 2025 23:19:50 +0100
Subject: [PATCH 3/7] revert

---
 aeon/transformations/collection/base.py | 16 +++++----
 aeon/transformations/series/base.py     | 47 +++++++++++++++++++------
 2 files changed, 47 insertions(+), 16 deletions(-)

diff --git a/aeon/transformations/collection/base.py b/aeon/transformations/collection/base.py
index 6eb9779b19..921e70e377 100644
--- a/aeon/transformations/collection/base.py
+++ b/aeon/transformations/collection/base.py
@@ -216,16 +216,21 @@ def _fit(self, X, y=None):
 
         private _fit containing the core logic, called from fit
 
-        private _transform containing the core logic, called from transform
-
         Parameters
         ----------
         X : Input data
             Data to fit transform to, of valid collection type.
         y : Target variable, default=None
             Additional data, e.g., labels for transformation
+
+        Returns
+        -------
+        self: a fitted instance of the estimator
+
+        See extension_templates/transformer.py for implementation details.
         """
-        ...
+        # default fit is "no fitting happens"
+        return self
 
     @abstractmethod
     def _transform(self, X, y=None):
@@ -253,9 +258,6 @@ def _fit_transform(self, X, y=None):
 
         private _fit_transform containing the core logic, called from fit_transform.
 
-        Non-optimized default implementation; override when a better
-        method is possible for a given algorithm.
-
         Parameters
         ----------
         X : Input data
@@ -267,6 +269,8 @@ def _fit_transform(self, X, y=None):
         -------
         transformed version of X.
         """
+        # Non-optimized default implementation; override when a better
+        # method is possible for a given algorithm.
         if not self.get_tag("fit_is_empty"):
             self._fit(X, y)
         return self._transform(X, y)
diff --git a/aeon/transformations/series/base.py b/aeon/transformations/series/base.py
index f89404d1f9..299b502e79 100644
--- a/aeon/transformations/series/base.py
+++ b/aeon/transformations/series/base.py
@@ -165,22 +165,48 @@ def fit_transform(self, X, y=None, axis=1):
         self.is_fitted = True
         return self._postprocess_series(Xt, axis=axis)
 
+    @final
+    def update(self, X, y=None, update_params=True, axis=1):
+        """Update transformer with X, optionally y.
+
+        Parameters
+        ----------
+        X : data to update of valid series type.
+        y : Target variable, default=None
+            Additional data, e.g., labels for transformation
+        update_params : bool, default=True
+            whether the model is updated. Yes if true, if false, simply skips call.
+            argument exists for compatibility with forecasting module.
+        axis : int, default=None
+            axis along which to update. If None, uses self.axis.
+
+        Returns
+        -------
+        self : a fitted instance of the estimator
+        """
+        # check whether is fitted
+        self._check_is_fitted()
+        X = self._preprocess_series(X, axis, False)
+        return self._update(X=X, y=y, update_params=update_params)
+
     def _fit(self, X, y=None):
         """Fit transformer to X and y.
 
         private _fit containing the core logic, called from fit
 
-        A default implementation for the fit_is_empty tag. Other transformers
-        should override this.
-
         Parameters
         ----------
         X : Input data
             Data to fit transform to, of valid collection type.
         y : Target variable, default=None
             Additional data, e.g., labels for transformation
+
+        Returns
+        -------
+        self: a fitted instance of the estimator
         """
-        ...
+        # default fit is "no fitting happens"
+        return self
 
     @abstractmethod
     def _transform(self, X, y=None):
@@ -199,7 +225,6 @@ def _transform(self, X, y=None):
         -------
         transformed version of X
         """
-        ...
 
     def _fit_transform(self, X, y=None):
         """Fit to data, then transform it.
@@ -208,9 +233,6 @@ def _fit_transform(self, X, y=None):
 
         private _fit_transform containing the core logic, called from fit_transform.
 
-        Non-optimised default implementation; override when a better
-        method is possible for a given algorithm.
-
         Parameters
         ----------
         X : Input data
@@ -222,10 +244,15 @@ def _fit_transform(self, X, y=None):
         -------
         transformed version of X.
         """
-        if not self.get_tag("fit_is_empty"):
-            self._fit(X, y)
+        # Non-optimized default implementation; override when a better
+        # method is possible for a given algorithm.
+        self._fit(X, y)
         return self._transform(X, y)
 
+    def _update(self, X, y=None, update_params=True):
+        # standard behaviour: no update takes place, new data is ignored
+        return self
+
     def _postprocess_series(self, Xt, axis):
         """Postprocess data Xt to revert to original shape.
 

From d93b4b31a4b7139ad06c82bd813657e901f8ae71 Mon Sep 17 00:00:00 2001
From: MatthewMiddlehurst <pfm15hbu@gmail.com>
Date: Tue, 26 Aug 2025 18:04:35 +0100
Subject: [PATCH 4/7] import

---
 aeon/transformations/base.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/aeon/transformations/base.py b/aeon/transformations/base.py
index fe2c8a5199..71087fca2f 100644
--- a/aeon/transformations/base.py
+++ b/aeon/transformations/base.py
@@ -1,7 +1,7 @@
 """Base class for transformers."""
 
 __maintainer__ = ["MatthewMiddlehurst", "TonyBagnall"]
-__all__ = ["BaseTransformer"]
+__all__ = ["BaseTransformer", "InverseTransformerMixin"]
 
 from abc import ABC, abstractmethod
 from typing import final
@@ -10,8 +10,8 @@
 import pandas as pd
 
 from aeon.base import BaseAeonEstimator
-from aeon.transformations.collection import BaseCollectionTransformer
-from aeon.transformations.series import BaseSeriesTransformer
+from aeon.transformations.collection.base import BaseCollectionTransformer
+from aeon.transformations.series.base import BaseSeriesTransformer
 
 
 class BaseTransformer(BaseAeonEstimator):

From 5c4dc94379445380147c3cd49bd0eba4626ca2cf Mon Sep 17 00:00:00 2001
From: MatthewMiddlehurst <pfm15hbu@gmail.com>
Date: Sun, 26 Oct 2025 19:49:24 +0000
Subject: [PATCH 5/7] two classes

---
 aeon/transformations/__init__.py              |  3 +-
 aeon/transformations/base.py                  | 19 +---
 aeon/transformations/collection/__init__.py   |  6 +-
 .../collection/_broadcaster.py                | 10 +-
 aeon/transformations/collection/base.py       | 62 ++++++++++++-
 .../collection/compose/_identity.py           |  4 +-
 aeon/transformations/series/__init__.py       | 10 +-
 aeon/transformations/series/_boxcox.py        |  8 +-
 aeon/transformations/series/_log.py           |  8 +-
 aeon/transformations/series/_scaled_logit.py  | 10 +-
 aeon/transformations/series/base.py           | 93 ++++++++++++++++++-
 .../series/compose/_identity.py               |  4 +-
 12 files changed, 195 insertions(+), 42 deletions(-)

diff --git a/aeon/transformations/__init__.py b/aeon/transformations/__init__.py
index 23421c062b..eae067f9cb 100644
--- a/aeon/transformations/__init__.py
+++ b/aeon/transformations/__init__.py
@@ -2,6 +2,7 @@
 
 __all__ = [
     "BaseTransformer",
+    "InverseTransformerMixin",
 ]
 
-from aeon.transformations.base import BaseTransformer
+from aeon.transformations.base import BaseTransformer, InverseTransformerMixin
diff --git a/aeon/transformations/base.py b/aeon/transformations/base.py
index 71087fca2f..9b55affebc 100644
--- a/aeon/transformations/base.py
+++ b/aeon/transformations/base.py
@@ -4,14 +4,11 @@
 __all__ = ["BaseTransformer", "InverseTransformerMixin"]
 
 from abc import ABC, abstractmethod
-from typing import final
 
 import numpy as np
 import pandas as pd
 
 from aeon.base import BaseAeonEstimator
-from aeon.transformations.collection.base import BaseCollectionTransformer
-from aeon.transformations.series.base import BaseSeriesTransformer
 
 
 class BaseTransformer(BaseAeonEstimator):
@@ -124,7 +121,7 @@ class InverseTransformerMixin(ABC):
         "capability:inverse_transform": True,
     }
 
-    @final
+    @abstractmethod
     def inverse_transform(self, X, y=None, axis=1):
         """Inverse transform X and return an inverse transformed version.
 
@@ -164,19 +161,7 @@ def inverse_transform(self, X, y=None, axis=1):
         inverse transformed version of X
             of the same type as X
         """
-        # check whether is fitted
-        self._check_is_fitted()
-
-        # input check and conversion for X/y
-        if isinstance(self, BaseCollectionTransformer):
-            X_inner = self._preprocess_collection(X, store_metadata=False)
-            Xt = self._inverse_transform(X=X_inner, y=y)
-            return Xt
-        elif isinstance(self, BaseSeriesTransformer):
-            self._check_is_fitted()
-            X = self._preprocess_series(X, axis=axis, store_metadata=False)
-            Xt = self._inverse_transform(X=X, y=y)
-            return self._postprocess_series(Xt, axis=axis)
+        ...
 
     @abstractmethod
     def _inverse_transform(self, X, y=None):
diff --git a/aeon/transformations/collection/__init__.py b/aeon/transformations/collection/__init__.py
index 19dddc6e99..306fb46f49 100644
--- a/aeon/transformations/collection/__init__.py
+++ b/aeon/transformations/collection/__init__.py
@@ -3,6 +3,7 @@
 __all__ = [
     # base class and series wrapper
     "BaseCollectionTransformer",
+    "CollectionInverseTransformerMixin",
     # transformers
     "AutocorrelationFunctionTransformer",
     "ARCoefficientTransformer",
@@ -32,4 +33,7 @@
 from aeon.transformations.collection._reduce import Tabularizer
 from aeon.transformations.collection._rescale import Centerer, MinMaxScaler, Normalizer
 from aeon.transformations.collection._slope import SlopeTransformer
-from aeon.transformations.collection.base import BaseCollectionTransformer
+from aeon.transformations.collection.base import (
+    BaseCollectionTransformer,
+    CollectionInverseTransformerMixin,
+)
diff --git a/aeon/transformations/collection/_broadcaster.py b/aeon/transformations/collection/_broadcaster.py
index 5df90a0c72..bf818ac125 100644
--- a/aeon/transformations/collection/_broadcaster.py
+++ b/aeon/transformations/collection/_broadcaster.py
@@ -5,13 +5,17 @@
 
 import numpy as np
 
-from aeon.transformations.base import InverseTransformerMixin
-from aeon.transformations.collection.base import BaseCollectionTransformer
+from aeon.transformations.collection.base import (
+    BaseCollectionTransformer,
+    CollectionInverseTransformerMixin,
+)
 from aeon.transformations.series.base import BaseSeriesTransformer
 from aeon.utils.validation.collection import get_n_cases
 
 
-class SeriesToCollectionBroadcaster(BaseCollectionTransformer, InverseTransformerMixin):
+class SeriesToCollectionBroadcaster(
+    BaseCollectionTransformer, CollectionInverseTransformerMixin
+):
     """Broadcast a ``BaseSeriesTransformer`` over a collection of time series.
 
     Uses the ``BaseSeriesTransformer`` passed in the constructor.  If the
diff --git a/aeon/transformations/collection/base.py b/aeon/transformations/collection/base.py
index 37ffb69d1e..55df22b0a2 100644
--- a/aeon/transformations/collection/base.py
+++ b/aeon/transformations/collection/base.py
@@ -20,15 +20,13 @@ class name: BaseCollectionTransformer
 """
 
 __maintainer__ = ["MatthewMiddlehurst"]
-__all__ = [
-    "BaseCollectionTransformer",
-]
+__all__ = ["BaseCollectionTransformer", "CollectionInverseTransformerMixin"]
 
 from abc import abstractmethod
 from typing import final
 
 from aeon.base import BaseCollectionEstimator
-from aeon.transformations.base import BaseTransformer
+from aeon.transformations.base import BaseTransformer, InverseTransformerMixin
 from aeon.utils.validation.collection import get_n_cases
 
 
@@ -265,3 +263,59 @@ def _fit_transform(self, X, y=None):
         if not self.get_tag("fit_is_empty"):
             self._fit(X, y)
         return self._transform(X, y)
+
+
+class CollectionInverseTransformerMixin(InverseTransformerMixin):
+    """Mixin for transformers that support inverse transformation."""
+
+    _tags = {
+        "capability:inverse_transform": True,
+    }
+
+    @final
+    def inverse_transform(self, X, y=None):
+        """Inverse transform X and return an inverse transformed version.
+
+        Currently it is assumed that only transformers with tags
+             "input_data_type"="Series", "output_data_type"="Series",
+        can have an inverse_transform.
+
+        State required:
+             Requires state to be "fitted".
+
+        Accesses in self:
+         _is_fitted : must be True
+         fitted model attributes (ending in "_") : accessed by _inverse_transform
+
+        Parameters
+        ----------
+        X : Series or Collection, any supported type
+            Data to fit transform to, of python type as follows:
+                Series: 2D np.ndarray shape (n_channels, n_timepoints)
+                Collection: 3D np.ndarray shape (n_cases, n_channels, n_timepoints)
+                or list of 2D np.ndarray, case i has shape (n_channels, n_timepoints_i)
+        y : Series, default=None
+            Additional data, e.g., labels for transformation.
+        axis : int, default = 1
+            Axis of time in the input series.
+            If ``axis == 0``, it is assumed each column is a time series and each row is
+            a time point. i.e. the shape of the data is ``(n_timepoints,
+            n_channels)``.
+            ``axis == 1`` indicates the time series are in rows, i.e. the shape of
+            the data is ``(n_channels, n_timepoints)`.``axis is None`` indicates
+            that the axis of X is the same as ``self.axis``.
+
+            Only relevant for ``aeon.transformations.series`` transformers.
+
+        Returns
+        -------
+        inverse transformed version of X
+            of the same type as X
+        """
+        # check whether is fitted
+        self._check_is_fitted()
+
+        # input check and conversion for X/y
+        X_inner = self._preprocess_collection(X, store_metadata=False)
+        Xt = self._inverse_transform(X=X_inner, y=y)
+        return Xt
diff --git a/aeon/transformations/collection/compose/_identity.py b/aeon/transformations/collection/compose/_identity.py
index e197fd2fa6..521417b771 100644
--- a/aeon/transformations/collection/compose/_identity.py
+++ b/aeon/transformations/collection/compose/_identity.py
@@ -1,11 +1,11 @@
 """Identity transformer."""
 
-from aeon.transformations.base import InverseTransformerMixin
 from aeon.transformations.collection import BaseCollectionTransformer
+from aeon.transformations.collection.base import CollectionInverseTransformerMixin
 from aeon.utils.data_types import COLLECTIONS_DATA_TYPES
 
 
-class CollectionId(BaseCollectionTransformer, InverseTransformerMixin):
+class CollectionId(BaseCollectionTransformer, CollectionInverseTransformerMixin):
     """Identity transformer, returns data unchanged in transform/inverse_transform."""
 
     _tags = {
diff --git a/aeon/transformations/series/__init__.py b/aeon/transformations/series/__init__.py
index 96d2c78958..c491479f36 100644
--- a/aeon/transformations/series/__init__.py
+++ b/aeon/transformations/series/__init__.py
@@ -1,8 +1,11 @@
 """Series transformations."""
 
 __all__ = [
-    "AutoCorrelationSeriesTransformer",
+    # base class
     "BaseSeriesTransformer",
+    "SeriesInverseTransformerMixin",
+    # transformers
+    "AutoCorrelationSeriesTransformer",
     "ClaSPTransformer",
     "Dobin",
     "MatrixProfileTransformer",
@@ -38,4 +41,7 @@
 from aeon.transformations.series._pla import PLASeriesTransformer
 from aeon.transformations.series._scaled_logit import ScaledLogitSeriesTransformer
 from aeon.transformations.series._warping import WarpingSeriesTransformer
-from aeon.transformations.series.base import BaseSeriesTransformer
+from aeon.transformations.series.base import (
+    BaseSeriesTransformer,
+    SeriesInverseTransformerMixin,
+)
diff --git a/aeon/transformations/series/_boxcox.py b/aeon/transformations/series/_boxcox.py
index 70d9ad636a..adc13afcde 100644
--- a/aeon/transformations/series/_boxcox.py
+++ b/aeon/transformations/series/_boxcox.py
@@ -8,8 +8,10 @@
 from scipy.special import boxcox, inv_boxcox
 from scipy.stats import boxcox_llf, distributions, variation
 
-from aeon.transformations.base import InverseTransformerMixin
-from aeon.transformations.series.base import BaseSeriesTransformer
+from aeon.transformations.series.base import (
+    BaseSeriesTransformer,
+    SeriesInverseTransformerMixin,
+)
 
 
 # copy-pasted from scipy 1.7.3 since it moved in 1.8.0 and broke this estimator
@@ -39,7 +41,7 @@ def _calc_uniform_order_statistic_medians(n):
     return v
 
 
-class BoxCoxTransformer(BaseSeriesTransformer, InverseTransformerMixin):
+class BoxCoxTransformer(BaseSeriesTransformer, SeriesInverseTransformerMixin):
     r"""Box-Cox power transform.
 
     Box-Cox transformation is a power transformation that is used to
diff --git a/aeon/transformations/series/_log.py b/aeon/transformations/series/_log.py
index b82f104430..fd345ccdf7 100644
--- a/aeon/transformations/series/_log.py
+++ b/aeon/transformations/series/_log.py
@@ -5,11 +5,13 @@
 
 import numpy as np
 
-from aeon.transformations.base import InverseTransformerMixin
-from aeon.transformations.series.base import BaseSeriesTransformer
+from aeon.transformations.series.base import (
+    BaseSeriesTransformer,
+    SeriesInverseTransformerMixin,
+)
 
 
-class LogTransformer(BaseSeriesTransformer, InverseTransformerMixin):
+class LogTransformer(BaseSeriesTransformer, SeriesInverseTransformerMixin):
     """Natural logarithm transformation.
 
     The Natural logarithm transformation can be used to make the data more normally
diff --git a/aeon/transformations/series/_scaled_logit.py b/aeon/transformations/series/_scaled_logit.py
index e94e659e89..2d6351bbbf 100644
--- a/aeon/transformations/series/_scaled_logit.py
+++ b/aeon/transformations/series/_scaled_logit.py
@@ -8,11 +8,15 @@
 
 import numpy as np
 
-from aeon.transformations.base import InverseTransformerMixin
-from aeon.transformations.series.base import BaseSeriesTransformer
+from aeon.transformations.series.base import (
+    BaseSeriesTransformer,
+    SeriesInverseTransformerMixin,
+)
 
 
-class ScaledLogitSeriesTransformer(BaseSeriesTransformer, InverseTransformerMixin):
+class ScaledLogitSeriesTransformer(
+    BaseSeriesTransformer, SeriesInverseTransformerMixin
+):
     r"""Scaled logit transform or Log transform.
 
     If both lower_bound and upper_bound are not None, a scaled logit transform is
diff --git a/aeon/transformations/series/base.py b/aeon/transformations/series/base.py
index a21cee172e..022bd3eca9 100644
--- a/aeon/transformations/series/base.py
+++ b/aeon/transformations/series/base.py
@@ -11,8 +11,10 @@ class name: BaseSeriesTransformer
 from abc import abstractmethod
 from typing import final
 
+from deprecated.sphinx import deprecated
+
 from aeon.base import BaseSeriesEstimator
-from aeon.transformations.base import BaseTransformer
+from aeon.transformations.base import BaseTransformer, InverseTransformerMixin
 
 
 class BaseSeriesTransformer(BaseSeriesEstimator, BaseTransformer):
@@ -262,3 +264,92 @@ def _postprocess_series(self, Xt, axis):
             return Xt
         else:
             return Xt.T
+
+    # TODO: Remove in v1.4.0
+    @deprecated(
+        version="1.3.0",
+        reason="update is deprecated for transformers and will be removed in v1.4.0.",
+        category=FutureWarning,
+    )
+    @final
+    def update(self, X, y=None, update_params=True, axis=1):
+        """Update transformer with X, optionally y.
+
+        Parameters
+        ----------
+        X : data to update of valid series type.
+        y : Target variable, default=None
+            Additional data, e.g., labels for transformation
+        update_params : bool, default=True
+            whether the model is updated. Yes if true, if false, simply skips call.
+            argument exists for compatibility with forecasting module.
+        axis : int, default=None
+            axis along which to update. If None, uses self.axis.
+
+        Returns
+        -------
+        self : a fitted instance of the estimator
+        """
+        # check whether is fitted
+        self._check_is_fitted()
+        X = self._preprocess_series(X, axis, False)
+        return self._update(X=X, y=y, update_params=update_params)
+
+    def _update(self, X, y=None, update_params=True):
+        # standard behaviour: no update takes place, new data is ignored
+        return self
+
+
+class SeriesInverseTransformerMixin(InverseTransformerMixin):
+    """Mixin for transformers that support inverse transformation."""
+
+    _tags = {
+        "capability:inverse_transform": True,
+    }
+
+    @final
+    def inverse_transform(self, X, y=None, axis=1):
+        """Inverse transform X and return an inverse transformed version.
+
+        Currently it is assumed that only transformers with tags
+             "input_data_type"="Series", "output_data_type"="Series",
+        can have an inverse_transform.
+
+        State required:
+             Requires state to be "fitted".
+
+        Accesses in self:
+         _is_fitted : must be True
+         fitted model attributes (ending in "_") : accessed by _inverse_transform
+
+        Parameters
+        ----------
+        X : Series or Collection, any supported type
+            Data to fit transform to, of python type as follows:
+                Series: 2D np.ndarray shape (n_channels, n_timepoints)
+                Collection: 3D np.ndarray shape (n_cases, n_channels, n_timepoints)
+                or list of 2D np.ndarray, case i has shape (n_channels, n_timepoints_i)
+        y : Series, default=None
+            Additional data, e.g., labels for transformation.
+        axis : int, default = 1
+            Axis of time in the input series.
+            If ``axis == 0``, it is assumed each column is a time series and each row is
+            a time point. i.e. the shape of the data is ``(n_timepoints,
+            n_channels)``.
+            ``axis == 1`` indicates the time series are in rows, i.e. the shape of
+            the data is ``(n_channels, n_timepoints)`.``axis is None`` indicates
+            that the axis of X is the same as ``self.axis``.
+
+            Only relevant for ``aeon.transformations.series`` transformers.
+
+        Returns
+        -------
+        inverse transformed version of X
+            of the same type as X
+        """
+        # check whether is fitted
+        self._check_is_fitted()
+
+        X = self._preprocess_series(X, axis=axis, store_metadata=False)
+        Xt = self._inverse_transform(X=X, y=y)
+        return self._postprocess_series(Xt, axis=axis)
diff --git a/aeon/transformations/series/compose/_identity.py b/aeon/transformations/series/compose/_identity.py
index c67d200c19..a026cc7816 100644
--- a/aeon/transformations/series/compose/_identity.py
+++ b/aeon/transformations/series/compose/_identity.py
@@ -1,11 +1,11 @@
 """Identity transformer."""
 
-from aeon.transformations.base import InverseTransformerMixin
 from aeon.transformations.series import BaseSeriesTransformer
+from aeon.transformations.series.base import SeriesInverseTransformerMixin
 from aeon.utils.data_types import VALID_SERIES_INNER_TYPES
 
 
-class SeriesId(BaseSeriesTransformer, InverseTransformerMixin):
+class SeriesId(BaseSeriesTransformer, SeriesInverseTransformerMixin):
     """Identity transformer, returns data unchanged in transform/inverse_transform."""
 
     _tags = {

From f552b12c8bc10d818fac390b219cc54510c9558b Mon Sep 17 00:00:00 2001
From: MatthewMiddlehurst <pfm15hbu@gmail.com>
Date: Sun, 26 Oct 2025 20:45:59 +0000
Subject: [PATCH 6/7] tests

---
 .../_yield_transformation_checks.py             | 17 ++++++++++++++++-
 .../collection/tests/test_base.py               |  7 +++++--
 2 files changed, 21 insertions(+), 3 deletions(-)

diff --git a/aeon/testing/estimator_checking/_yield_transformation_checks.py b/aeon/testing/estimator_checking/_yield_transformation_checks.py
index f01d2b5370..178ec5e167 100644
--- a/aeon/testing/estimator_checking/_yield_transformation_checks.py
+++ b/aeon/testing/estimator_checking/_yield_transformation_checks.py
@@ -10,8 +10,12 @@
 from aeon.testing.testing_data import FULL_TEST_DATA_DICT
 from aeon.testing.utils.deep_equals import deep_equals
 from aeon.testing.utils.estimator_checks import _run_estimator_method
+from aeon.transformations.collection import CollectionInverseTransformerMixin
 from aeon.transformations.collection.channel_selection.base import BaseChannelSelector
-from aeon.transformations.series import BaseSeriesTransformer
+from aeon.transformations.series import (
+    BaseSeriesTransformer,
+    SeriesInverseTransformerMixin,
+)
 from aeon.utils.data_types import COLLECTIONS_DATA_TYPES, VALID_SERIES_INNER_TYPES
 
 
@@ -82,8 +86,19 @@ def check_transformer_overrides_and_tags(estimator_class):
         else:  # must be a list
             assert any([t in valid_unequal_types for t in X_inner_type])
 
+    inherits_inverse = (
+        issubclass(estimator_class, SeriesInverseTransformerMixin)
+        if issubclass(estimator_class, BaseSeriesTransformer)
+        else issubclass(estimator_class, CollectionInverseTransformerMixin)
+    )
     if estimator_class.get_class_tag("capability:inverse_transform"):
+        assert inherits_inverse
+        assert "inverse_transform" not in estimator_class.__dict__
         assert "_inverse_transform" in estimator_class.__dict__
+    else:
+        assert not inherits_inverse
+        assert "inverse_transform" not in estimator_class.__dict__
+        assert "_inverse_transform" not in estimator_class.__dict__
 
 
 def check_transformer_output(estimator, datatype):
diff --git a/aeon/transformations/collection/tests/test_base.py b/aeon/transformations/collection/tests/test_base.py
index c4aac11407..602a99902a 100644
--- a/aeon/transformations/collection/tests/test_base.py
+++ b/aeon/transformations/collection/tests/test_base.py
@@ -11,7 +11,10 @@
     make_example_3d_numpy_list,
     make_example_pandas_series,
 )
-from aeon.transformations.collection import BaseCollectionTransformer
+from aeon.transformations.collection import (
+    BaseCollectionTransformer,
+    CollectionInverseTransformerMixin,
+)
 
 
 @pytest.mark.parametrize(
@@ -55,7 +58,7 @@ def test_raise_inverse_transform():
         d.inverse_transform(x)
 
 
-class _Dummy(BaseCollectionTransformer):
+class _Dummy(BaseCollectionTransformer, CollectionInverseTransformerMixin):
     """Dummy transformer for testing.
 
     Converts a numpy array to a list of numpy arrays.

From 5687d8236cf32fe7a7825fae2a5a33ce5f243461 Mon Sep 17 00:00:00 2001
From: MatthewMiddlehurst <pfm15hbu@gmail.com>
Date: Sun, 26 Oct 2025 21:27:36 +0000
Subject: [PATCH 7/7] swap inherit order and remove unneeded test

---
 aeon/transformations/collection/_broadcaster.py     |  2 +-
 .../transformations/collection/compose/_identity.py |  2 +-
 aeon/transformations/collection/tests/test_base.py  | 13 +------------
 aeon/transformations/series/_boxcox.py              |  2 +-
 aeon/transformations/series/_log.py                 |  2 +-
 aeon/transformations/series/_scaled_logit.py        |  2 +-
 aeon/transformations/series/compose/_identity.py    |  2 +-
 7 files changed, 7 insertions(+), 18 deletions(-)

diff --git a/aeon/transformations/collection/_broadcaster.py b/aeon/transformations/collection/_broadcaster.py
index bf818ac125..9a4c36faa0 100644
--- a/aeon/transformations/collection/_broadcaster.py
+++ b/aeon/transformations/collection/_broadcaster.py
@@ -14,7 +14,7 @@
 
 
 class SeriesToCollectionBroadcaster(
-    BaseCollectionTransformer, CollectionInverseTransformerMixin
+    CollectionInverseTransformerMixin, BaseCollectionTransformer
 ):
     """Broadcast a ``BaseSeriesTransformer`` over a collection of time series.
 
diff --git a/aeon/transformations/collection/compose/_identity.py b/aeon/transformations/collection/compose/_identity.py
index 521417b771..12f5c4a3aa 100644
--- a/aeon/transformations/collection/compose/_identity.py
+++ b/aeon/transformations/collection/compose/_identity.py
@@ -5,7 +5,7 @@
 from aeon.utils.data_types import COLLECTIONS_DATA_TYPES
 
 
-class CollectionId(BaseCollectionTransformer, CollectionInverseTransformerMixin):
+class CollectionId(CollectionInverseTransformerMixin, BaseCollectionTransformer):
     """Identity transformer, returns data unchanged in transform/inverse_transform."""
 
     _tags = {
diff --git a/aeon/transformations/collection/tests/test_base.py b/aeon/transformations/collection/tests/test_base.py
index 602a99902a..ada1cf2325 100644
--- a/aeon/transformations/collection/tests/test_base.py
+++ b/aeon/transformations/collection/tests/test_base.py
@@ -47,18 +47,7 @@ def test_collection_transformer_invalid_input(dtype):
         t.fit_transform(y)
 
 
-def test_raise_inverse_transform():
-    """Test that inverse transform raises NotImplementedError."""
-    d = _Dummy()
-    x, _ = make_example_3d_numpy()
-    d.fit(x)
-    with pytest.raises(
-        NotImplementedError, match="does not implement " "inverse_transform"
-    ):
-        d.inverse_transform(x)
-
-
-class _Dummy(BaseCollectionTransformer, CollectionInverseTransformerMixin):
+class _Dummy(CollectionInverseTransformerMixin, BaseCollectionTransformer):
     """Dummy transformer for testing.
 
     Converts a numpy array to a list of numpy arrays.
diff --git a/aeon/transformations/series/_boxcox.py b/aeon/transformations/series/_boxcox.py
index adc13afcde..9f9a926c6b 100644
--- a/aeon/transformations/series/_boxcox.py
+++ b/aeon/transformations/series/_boxcox.py
@@ -41,7 +41,7 @@ def _calc_uniform_order_statistic_medians(n):
     return v
 
 
-class BoxCoxTransformer(BaseSeriesTransformer, SeriesInverseTransformerMixin):
+class BoxCoxTransformer(SeriesInverseTransformerMixin, BaseSeriesTransformer):
     r"""Box-Cox power transform.
 
     Box-Cox transformation is a power transformation that is used to
diff --git a/aeon/transformations/series/_log.py b/aeon/transformations/series/_log.py
index fd345ccdf7..d2e0cabead 100644
--- a/aeon/transformations/series/_log.py
+++ b/aeon/transformations/series/_log.py
@@ -11,7 +11,7 @@
 )
 
 
-class LogTransformer(BaseSeriesTransformer, SeriesInverseTransformerMixin):
+class LogTransformer(SeriesInverseTransformerMixin, BaseSeriesTransformer):
     """Natural logarithm transformation.
 
     The Natural logarithm transformation can be used to make the data more normally
diff --git a/aeon/transformations/series/_scaled_logit.py b/aeon/transformations/series/_scaled_logit.py
index 2d6351bbbf..69e8f2127d 100644
--- a/aeon/transformations/series/_scaled_logit.py
+++ b/aeon/transformations/series/_scaled_logit.py
@@ -15,7 +15,7 @@
 
 
 class ScaledLogitSeriesTransformer(
-    BaseSeriesTransformer, SeriesInverseTransformerMixin
+    SeriesInverseTransformerMixin, BaseSeriesTransformer
 ):
     r"""Scaled logit transform or Log transform.
 
diff --git a/aeon/transformations/series/compose/_identity.py b/aeon/transformations/series/compose/_identity.py
index a026cc7816..b186b6fb70 100644
--- a/aeon/transformations/series/compose/_identity.py
+++ b/aeon/transformations/series/compose/_identity.py
@@ -5,7 +5,7 @@
 from aeon.utils.data_types import VALID_SERIES_INNER_TYPES
 
 
-class SeriesId(BaseSeriesTransformer, SeriesInverseTransformerMixin):
+class SeriesId(SeriesInverseTransformerMixin, BaseSeriesTransformer):
     """Identity transformer, returns data unchanged in transform/inverse_transform."""
 
     _tags = {