diff --git a/aeon/transformations/bkfilter.py b/aeon/transformations/bkfilter.py
deleted file mode 100644
index 0071de97ea..0000000000
--- a/aeon/transformations/bkfilter.py
+++ /dev/null
@@ -1,147 +0,0 @@
-"""
-Implements Baxter-King bandpass filter transformation.
-
-Please see the original library
-(https://github.com/statsmodels/statsmodels/blob/main/statsmodels/tsa/filters/bk_filter.py)
-"""
-
-__maintainer__ = []
-__all__ = ["BKFilter"]
-
-
-import numpy as np
-import pandas as pd
-from deprecated.sphinx import deprecated
-
-from aeon.transformations.base import BaseTransformer
-
-
-# TODO: remove in v0.10.0
-@deprecated(
-    version="0.9.0",
-    reason="BKFilter will be removed in version 0.10 and replaced with a "
-    "BaseSeriesTransformer version in the transformations.series module.",
-    category=FutureWarning,
-)
-class BKFilter(BaseTransformer):
-    """Filter a times series using the Baxter-King filter.
-
-    This is a wrapper around statsmodels' bkfilter function
-    (see 'sm.tsa.filters.bk_filter.bkfilter').
-
-    The Baxter-King filter is intended for economic and econometric time series
-    data and deals with the periodicity of the business cycle. Applying their
-    band-pass filter to a series will produce a new series that does not contain
-    fluctuations at a higher or lower frequency than those of the business cycle.
-    Baxter-King follow Burns and Mitchell's work on business cycles, which suggests
-    that U.S. business cycles typically last from 1.5 to 8 years.
-
-    Parameters
-    ----------
-    low : float
-        Minimum period for oscillations. Baxter and King recommend a value of 6
-        for quarterly data and 1.5 for annual data.
-
-    high : float
-        Maximum period for oscillations. BK recommend 32 for U.S. business cycle
-        quarterly data and 8 for annual data.
-
-    K : int
-        Lead-lag length of the filter. Baxter and King suggest a truncation
-        length of 12 for quarterly data and 3 for annual data.
-
-    Notes
-    -----
-    Returns a centered weighted moving average of the original series.
-
-    References
-    ----------
-    Baxter, M. and R. G. King. "Measuring Business Cycles: Approximate
-        Band-Pass Filters for Economic Time Series." *Review of Economics and
-        Statistics*, 1999, 81(4), 575-593.
-    """
-
-    _tags = {
-        "input_data_type": "Series",
-        # what is the abstract type of X: Series, or Panel
-        "output_data_type": "Series",
-        # what abstract type is returned: Primitives, Series, Panel
-        "instancewise": True,
-        "capability:multivariate": True,
-        "X_inner_type": "np.ndarray",
-        "y_inner_type": "None",
-        "enforce_index_type": [pd.RangeIndex],
-        "fit_is_empty": True,
-        "transform-returns-same-time-index": False,
-        "capability:unequal_length": True,
-        "capability:missing_values": False,
-        "remember_data": False,
-        "python_dependencies": "statsmodels",
-    }
-
-    def __init__(
-        self,
-        low=6,
-        high=32,
-        K=12,
-    ):
-        self.low = low
-        self.high = high
-        self.K = K
-        super().__init__()
-
-    def _transform(self, X, y=None):
-        """Transform X and return a transformed version.
-
-        private _transform containing core logic, called from transform
-
-        Parameters
-        ----------
-        X : array_like
-        A 1 or 2d ndarray. If 2d, variables are assumed to be in columns.
-
-        Returns
-        -------
-        transformed cyclical version of X
-        """
-        from scipy.signal import fftconvolve
-        from statsmodels.tools.validation import PandasWrapper, array_like
-
-        pw = PandasWrapper(X)
-        X = array_like(X, "X", maxdim=2)
-        omega_1 = 2.0 * np.pi / self.high
-        omega_2 = 2.0 * np.pi / self.low
-        bweights = np.zeros(2 * self.K + 1)
-        bweights[self.K] = (omega_2 - omega_1) / np.pi
-        j = np.arange(1, int(self.K) + 1)
-        weights = 1 / (np.pi * j) * (np.sin(omega_2 * j) - np.sin(omega_1 * j))
-        bweights[self.K + j] = weights
-        bweights[: self.K] = weights[::-1]
-        bweights -= bweights.mean()
-        if X.ndim == 2:
-            bweights = bweights[:, None]
-        X = fftconvolve(X, bweights, mode="valid")
-
-        return pw.wrap(X, append="cycle", trim_start=self.K, trim_end=self.K)
-
-    @classmethod
-    def get_test_params(cls, parameter_set="default"):
-        """Return testing parameter settings for the estimator.
-
-        Parameters
-        ----------
-        parameter_set : str, default="default"
-            Name of the set of test parameters to return, for use in tests. If no
-            special parameters are defined for a value, will return `"default"` set.
-            There are currently no reserved values for transformers.
-
-        Returns
-        -------
-        params : dict or list of dict, default = {}
-            Parameters to create testing instances of the class
-            Each dict are parameters to construct an "interesting" test instance, i.e.,
-            `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`
-        """
-        params = {"low": 6, "high": 24, "K": 12}
-        return params
diff --git a/aeon/transformations/series/__init__.py b/aeon/transformations/series/__init__.py
index 3427971881..1f71c6f592 100644
--- a/aeon/transformations/series/__init__.py
+++ b/aeon/transformations/series/__init__.py
@@ -10,6 +10,7 @@
     "StatsModelsACF",
     "StatsModelsPACF",
     "ThetaTransformer",
+    "BKFilter",
 ]
 
 from aeon.transformations.series._acf import (
@@ -17,6 +18,7 @@
     StatsModelsACF,
     StatsModelsPACF,
 )
+from aeon.transformations.series._bkfilter import BKFilter
 from aeon.transformations.series._clasp import ClaSPTransformer
 from aeon.transformations.series._clear_sky import ClearSkyTransformer
 from aeon.transformations.series._dobin import Dobin