diff --git a/aeon/transformations/acf.py b/aeon/transformations/acf.py
index 4c109ed9c6..66992f1a74 100644
--- a/aeon/transformations/acf.py
+++ b/aeon/transformations/acf.py
@@ -18,7 +18,7 @@
 @deprecated(
     version="0.9.0",
     reason="AutoCorrelationTransformer statsmodels wrapper will be removed in version "
-    "0.10. Please use the BaseSeriesTransformer version in the "
+    "0.10.0. Please use the BaseSeriesTransformer version in the "
     "transformations.series module called StatsModelsACF for a wrapper version.",
     category=FutureWarning,
 )
@@ -156,8 +156,8 @@ def get_test_params(cls, parameter_set="default"):
 @deprecated(
     version="0.9.0",
     reason="PartialAutoCorrelationTransformer statsmodels wrapper will be removed"
-    "in version 0.10. Please use the BaseSeriesTransformer version in the "
-    "transformations.series module called StatsModelsACF.",
+    "in version 0.10.0. Please use the BaseSeriesTransformer version in the "
+    "transformations.series module called StatsModelsPACF.",
     category=FutureWarning,
 )
 class PartialAutoCorrelationTransformer(BaseTransformer):
diff --git a/aeon/transformations/augmenter.py b/aeon/transformations/augmenter.py
index 59fe847789..fa98c5ed8c 100644
--- a/aeon/transformations/augmenter.py
+++ b/aeon/transformations/augmenter.py
@@ -37,7 +37,7 @@ class _AugmenterTags:
 # TODO: remove in v0.10.0
 @deprecated(
     version="0.9.0",
-    reason="WhiteNoiseAugmenter will be removed in version 0.10.",
+    reason="WhiteNoiseAugmenter will be removed in version 0.10.0.",
     category=FutureWarning,
 )
 class WhiteNoiseAugmenter(_AugmenterTags, BaseTransformer):
@@ -89,7 +89,7 @@ def _transform(self, X, y=None):
 # TODO: remove in v0.10.0
 @deprecated(
     version="0.9.0",
-    reason="ReverseAugmenter will be removed in version 0.10.",
+    reason="ReverseAugmenter will be removed in version 0.10.0.",
     category=FutureWarning,
 )
 class ReverseAugmenter(_AugmenterTags, BaseTransformer):
@@ -129,7 +129,7 @@ def _transform(self, X, y=None):
 # TODO: remove in v0.10.0
 @deprecated(
     version="0.9.0",
-    reason="InvertAugmenter will be removed in version 0.10.",
+    reason="InvertAugmenter will be removed in version 0.10.0.",
     category=FutureWarning,
 )
 class InvertAugmenter(_AugmenterTags, BaseTransformer):
@@ -162,7 +162,7 @@ def _transform(self, X, y=None):
 # TODO: remove in v0.10.0
 @deprecated(
     version="0.9.0",
-    reason="RandomSamplesAugmenter will be removed in version 0.10.",
+    reason="RandomSamplesAugmenter will be removed in version 0.10.0.",
     category=FutureWarning,
 )
 class RandomSamplesAugmenter(_AugmenterTags, BaseTransformer):
diff --git a/aeon/transformations/binning.py b/aeon/transformations/binning.py
index 4c130e1d7c..056b5c855e 100644
--- a/aeon/transformations/binning.py
+++ b/aeon/transformations/binning.py
@@ -14,7 +14,7 @@
 # TODO: remove in v0.10.0
 @deprecated(
     version="0.9.0",
-    reason="TimeBinAggregate will be removed in version 0.10.",
+    reason="TimeBinAggregate will be removed in version 0.10.0.",
     category=FutureWarning,
 )
 class TimeBinAggregate(BaseTransformer):
diff --git a/aeon/transformations/boxcox.py b/aeon/transformations/boxcox.py
index 9d7666cf4f..5a89842354 100644
--- a/aeon/transformations/boxcox.py
+++ b/aeon/transformations/boxcox.py
@@ -44,7 +44,7 @@ def _calc_uniform_order_statistic_medians(n):
 # TODO: remove in v0.10.0
 @deprecated(
     version="0.9.0",
-    reason="BoxCoxTransformer will be removed in version 0.10 and replaced with a "
+    reason="BoxCoxTransformer will be removed in version 0.10.0 and replaced with a "
     "BaseSeriesTransformer version in the transformations.series module.",
     category=FutureWarning,
 )
@@ -217,7 +217,7 @@ def _inverse_transform(self, X, y=None):
 # TODO: remove in v0.10.0
 @deprecated(
     version="0.9.0",
-    reason="LogTransformer will be removed in version 0.10 and replaced with a "
+    reason="LogTransformer will be removed in version 0.10.0 and replaced with a "
     "BaseSeriesTransformer version in the transformations.series module.",
     category=FutureWarning,
 )
diff --git a/aeon/transformations/clear_sky.py b/aeon/transformations/clear_sky.py
index 5cc9cdbf2c..344fbb933e 100644
--- a/aeon/transformations/clear_sky.py
+++ b/aeon/transformations/clear_sky.py
@@ -4,12 +4,20 @@
 
 import numpy as np
 import pandas as pd
+from deprecated.sphinx import deprecated
 from joblib import Parallel, delayed
 from scipy.stats import vonmises
 
 from aeon.transformations.base import BaseTransformer
 
 
+# TODO: remove in v0.10.0
+@deprecated(
+    version="0.9.0",
+    reason="ClearSky will be removed in version 0.10.0 and replaced with a "
+    "BaseSeriesTransformer version in the transformations.series module.",
+    category=FutureWarning,
+)
 class ClearSky(BaseTransformer):
     """Clear sky transformer for solar data.
 
diff --git a/aeon/transformations/cos.py b/aeon/transformations/cos.py
index 9a539097c1..cb6040243e 100644
--- a/aeon/transformations/cos.py
+++ b/aeon/transformations/cos.py
@@ -12,8 +12,7 @@
 # TODO: remove in v0.10.0
 @deprecated(
     version="0.9.0",
-    reason="CosineTransformer scikit wrapper will be removed in version 0.10. Please "
-    "just use scikit learn.",
+    reason="CosineTransformer will be removed in version 0.10.",
     category=FutureWarning,
 )
 class CosineTransformer(BaseTransformer):
diff --git a/aeon/transformations/difference.py b/aeon/transformations/difference.py
index 073a73f7d1..81b6cfcb45 100644
--- a/aeon/transformations/difference.py
+++ b/aeon/transformations/difference.py
@@ -172,7 +172,7 @@ def _inverse_diff(X, lags, X_diff_seq=None):
 # TODO: remove v0.10.0
 @deprecated(
     version="0.9.0",
-    reason="The Differencer class will be removed in C0.10.0, see issue #1534",
+    reason="The Differencer class will be removed in v0.10.0, see issue #1534",
     category=FutureWarning,
 )
 class Differencer(BaseTransformer):
diff --git a/aeon/transformations/dobin.py b/aeon/transformations/dobin.py
index 4ac9f6ab49..69adbef755 100644
--- a/aeon/transformations/dobin.py
+++ b/aeon/transformations/dobin.py
@@ -20,7 +20,7 @@
 # TODO: remove in v0.10.0
 @deprecated(
     version="0.9.0",
-    reason="DOBIN will be removed in version 0.10 and replaced with a "
+    reason="DOBIN will be removed in version 0.10.0 and replaced with a "
     "BaseSeriesTransformer version in the transformations.series module.",
     category=FutureWarning,
 )
diff --git a/aeon/transformations/exponent.py b/aeon/transformations/exponent.py
index 3357130326..b85f912e91 100644
--- a/aeon/transformations/exponent.py
+++ b/aeon/transformations/exponent.py
@@ -189,7 +189,7 @@ def get_test_params(cls, parameter_set="default"):
 # TODO: remove in v0.10.0
 @deprecated(
     version="0.9.0",
-    reason="SqrtTransformer will be removed in version 0.10.",
+    reason="SqrtTransformer will be removed in version 0.10.0.",
     category=FutureWarning,
 )
 class SqrtTransformer(ExponentTransformer):
diff --git a/aeon/transformations/series/__init__.py b/aeon/transformations/series/__init__.py
index 43d0891e93..24baf8bed1 100644
--- a/aeon/transformations/series/__init__.py
+++ b/aeon/transformations/series/__init__.py
@@ -3,6 +3,7 @@
 __all__ = [
     "AutoCorrelationSeriesTransformer",
     "BaseSeriesTransformer",
+    "ClearSkyTransformer",
     "Dobin",
     "MatrixProfileSeriesTransformer",
     "StatsModelsACF",
@@ -15,6 +16,7 @@
     StatsModelsACF,
     StatsModelsPACF,
 )
+from aeon.transformations.series._clear_sky import ClearSkyTransformer
 from aeon.transformations.series._dobin import Dobin
 from aeon.transformations.series._matrix_profile import MatrixProfileSeriesTransformer
 from aeon.transformations.series._theta import ThetaTransformer
diff --git a/aeon/transformations/series/_clear_sky.py b/aeon/transformations/series/_clear_sky.py
new file mode 100644
index 0000000000..ffdcfa3f4f
--- /dev/null
+++ b/aeon/transformations/series/_clear_sky.py
@@ -0,0 +1,346 @@
+"""Clear sky transformer for solar time-series."""
+
+__maintainer__ = []
+
+import numpy as np
+import pandas as pd
+from joblib import Parallel, delayed
+from scipy.stats import vonmises
+
+from aeon.transformations.series.base import BaseSeriesTransformer
+
+
+class ClearSkyTransformer(BaseSeriesTransformer):
+    """Clear sky transformer for solar data.
+
+    This is a transformation which converts a time series from it's original
+    domain into a percentage domain. The numerator at each time step in the
+    transformation is the input values, the denominator is a weighted
+    quantile of the time series for that particular time step. In the example
+    of solar power transformations, the denominator is an approximation of the
+    clear sky power, and the output of the transformation is the clearness index.
+
+    The clear sky power, i.e. the denominator, is calculated on a grid containing
+    each unique combination of time-of-day and day-of-year. The spacing of the
+    grid depends on the frequency of the input data.
+
+    The weights are defined using von-mises kernels with bandwidths chosen by the
+    user.
+
+    This transformation can be inacurate at low values, in the solar example during
+    early morning and late evening. Therefore, clear sky values below a threshold can
+    be fixed to zero in the transformed domain. Denominator values of zero are set
+    to zero in the transformed domain by default.
+
+    This transformer is based on the work detailed in [1]_.
+
+    Parameters
+    ----------
+    quantile_prob : float, default=0.95
+        The probability level used to calculate the weighted quantile
+    bw_diurnal : float, default=100
+        The bandwidth of the diurnal kernel. This is the kappa value of the
+        von mises kernel for time of day.
+    bw_annual : float, default=10
+        The bandwidth of the annual kernel. This is the kappa value of the
+        von mises kernel for day of year.
+    min_thresh : float, default=0
+        The threshold of the clear sky power below which values are
+        set to zero in the transformed domain.
+    n_jobs : int or None, default=None
+        Number of jobs to run in parallel.
+        None means 1 unless in a joblib.parallel_backend context.
+        -1 means using all processors.
+    backend : str, default="loky"
+        Specify the parallelisation backend implementation in joblib, where
+        "loky" is used by default.
+
+    References
+    ----------
+    .. [1] https://doi.org/10.1016/j.solener.2009.05.016
+
+    Examples
+    --------
+    >>> from aeon.transformations.series import ClearSkyTransformer  # doctest: +SKIP
+    >>> from aeon.datasets import load_solar  # doctest: +SKIP
+    >>> y = load_solar()  # doctest: +SKIP
+    >>> transformer = ClearSkyTransformer()  # doctest: +SKIP
+    >>> # takes ~1min
+    >>> y_trafo = transformer.fit_transform(y)  # doctest: +SKIP
+    """
+
+    _tags = {
+        "X_inner_type": "pd.Series",
+        "enforce_index_type": [pd.DatetimeIndex, pd.PeriodIndex],
+        "transform-returns-same-time-index": True,
+        "skip-inverse-transform": False,
+        "capability:inverse_transform": True,
+        "capability:multivariate": False,
+        "capability:missing_values": True,
+        "capability:missing_values:removes": True,
+        "python_dependencies": "statsmodels",
+    }
+
+    def __init__(
+        self,
+        quantile_prob=0.95,
+        bw_diurnal=100,
+        bw_annual=10,
+        min_thresh=0,
+        n_jobs=None,
+        backend="loky",
+    ):
+        self.quantile_prob = quantile_prob
+        self.bw_diurnal = bw_diurnal
+        self.bw_annual = bw_annual
+        self.min_thresh = min_thresh
+        self.n_jobs = n_jobs
+        self.backend = backend
+
+        super().__init__(axis=0)
+
+    def _fit(self, X, y=None):
+        """Fit transformer to X and y.
+
+        private _fit containing the core logic, called from fit
+
+        Parameters
+        ----------
+        X : Series of pd.DataFrame
+            Data used to estimate the clear sky power.
+        y : Ignored argument for interface compatibility.
+
+        Returns
+        -------
+        self: reference to self
+        """
+        # check that the data is formatted correctly etc
+        self.freq = _check_index(X)
+        # now get grid of model
+        df = pd.DataFrame(index=X.index)
+        df["yday"] = df.index.dayofyear
+        df["tod"] = df.index.hour + df.index.minute / 60 + df.index.second / 60
+
+        # set up smoothing grid
+        tod = pd.timedelta_range(start="0T", end="1D", freq=self.freq)[:-1]
+        tod = [(x.total_seconds() / (60 * 60)) for x in tod.to_pytimedelta()]
+        yday = pd.RangeIndex(start=1, stop=367)
+        indx = pd.MultiIndex.from_product([yday, tod], names=["yday", "tod"])
+
+        # set up parallel function and backend
+        parallel = Parallel(n_jobs=self.n_jobs, backend=self.backend)
+
+        def par_csp(x):
+            res = _clearskypower(
+                y=X,
+                q=self.quantile_prob,
+                tod_i=x[1],
+                doy_i=x[0],
+                tod_vec=df["tod"],
+                doy_vec=df["yday"],
+                bw_tod=self.bw_diurnal,
+                bw_doy=self.bw_annual,
+            )
+
+            return res
+
+        # calculate the csp
+        csp = parallel(delayed(par_csp)(name) for name in indx)
+        csp = pd.Series(csp, index=indx, dtype="float64")
+        self.clearskypower = csp.sort_index()
+
+        return self
+
+    def _transform(self, X, y=None):
+        """Transform X and return a transformed version.
+
+        private _transform containing core logic, called from transform
+
+        Parameters
+        ----------
+        X : Series of pd.DataFrame
+            Data used to be transformed.
+        y : Ignored argument for interface compatibility.
+
+        Returns
+        -------
+        X_trafo : transformed version of X
+        """
+        _freq_ind = _check_index(X)
+        if self.freq != _freq_ind:
+            raise ValueError(
+                """
+                Change in frequency detected from original input. Make sure
+                X is the same frequency as used in .fit().
+                """
+            )
+        # get required seasonal index
+        yday = X.index.dayofyear
+        tod = X.index.hour + X.index.minute / 60 + X.index.second / 60
+        indx_seasonal = pd.MultiIndex.from_arrays([yday, tod], names=["yday", "tod"])
+
+        # look up values and overwrite index
+        csp = self.clearskypower[indx_seasonal].copy()
+        csp.index = X.index
+        X_trafo = X / csp
+
+        # threshold for small morning/evening values
+        X_trafo[(csp <= self.min_thresh) & (X.notnull())] = 0
+
+        return X_trafo
+
+    def _inverse_transform(self, X, y=None):
+        """Inverse transform, inverse operation to transform.
+
+        private _inverse_transform containing core logic, called from
+        inverse_transform
+
+        Parameters
+        ----------
+        X : Series of pd.DataFrame
+            Data used to be inversed transformed.
+        y : Ignored argument for interface compatibility.
+
+        Returns
+        -------
+        X_trafo : inverse transformed version of X
+        """
+        _freq_ind = _check_index(X)
+        if self.freq != _freq_ind:
+            raise ValueError(
+                """
+                Change in frequency detected from original input. Make sure
+                X is the same frequency as used in .fit().
+                """
+            )
+        yday = X.index.dayofyear
+        tod = X.index.hour + X.index.minute / 60 + X.index.second / 60
+        indx_seasonal = pd.MultiIndex.from_arrays([yday, tod], names=["yday", "tod"])
+
+        # look up values and overwrite index
+        csp = self.clearskypower[indx_seasonal].copy()
+        csp.index = X.index
+        X_trafo = X * csp
+
+        return X_trafo
+
+    def _get_fitted_params(self):
+        """Get fitted parameters.
+
+        Returns
+        -------
+        fitted_params : dict
+        """
+        params = {"clearskypower": self.clearskypower}
+
+        return params
+
+    @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 = {
+            "quantile_prob": 0.95,
+            "bw_diurnal": 100,
+            "bw_annual": 10,
+            "min_thresh": None,
+        }
+
+        return params
+
+
+def _clearskypower(y, q, tod_i, doy_i, tod_vec, doy_vec, bw_tod, bw_doy):
+    """Estimate the clear sky power for a given day-of-year and hour-of-day.
+
+    Parameters
+    ----------
+    y : Series of measurements
+    q : Probability level used for the quantile
+    tod_i : time-of-day of interest in hours
+    doy_i : day-of-year of interest in days
+    tod_vec : Series of time-of-day corresponding to the index of y
+    doy_vec: Series of day-of-year corresponding to the index of y
+    bw_tod : Kappa value used for defining weights for time-of-day
+    bw_doy : Kappa value used for defining weights for day-of-year
+
+    Returns
+    -------
+    csp : float
+        The clear sky power at tod_i and doy_i
+    """
+    from statsmodels.stats.weightstats import DescrStatsW
+
+    wts_tod = vonmises.pdf(
+        x=tod_i * 2 * np.pi / 24, kappa=bw_tod, loc=tod_vec * 2 * np.pi / 24
+    )
+    wts_doy = vonmises.pdf(
+        x=doy_i * 2 * np.pi / 365.25, kappa=bw_doy, loc=doy_vec * 2 * np.pi / 365.25
+    )
+
+    wts = wts_doy * wts_tod
+    wts = wts / wts.sum()
+
+    csp = DescrStatsW(y, weights=wts).quantile(probs=q).values[0]
+
+    return csp
+
+
+def _check_index(X):
+    """Check input value frequency is set and we have the correct index.
+
+    Parameters
+    ----------
+    X : Series or pd.DataFrame
+        Data used to be inversed transformed.
+
+    Raises
+    ------
+    ValueError : Input index must be class pd.DatetimeIndex or pd.PeriodIndex.
+    ValueError : Input index frequency cannot be inferred and is not set.
+    ValueError : Frequency of data not suitable for transformer as is.
+
+    Returns
+    -------
+    freq_ind : str or None
+        Frequency of data in string format
+
+    """
+    if not (isinstance(X.index, pd.DatetimeIndex)) | (
+        isinstance(X.index, pd.PeriodIndex)
+    ):
+        raise ValueError(
+            "Input index must be class pd.DatetimeIndex or pd.PeriodIndex."
+        )
+    # check that it has a frequency, if not infer
+    freq_ind = X.index.freq
+    if freq_ind is None:
+        freq_ind = pd.infer_freq(X.index)
+        if freq_ind is None:
+            raise ValueError("Input index frequency cannot be inferred and is not set.")
+
+    tod = pd.timedelta_range(start="0T", end="1D", freq=freq_ind)
+    # checck frequency of tod
+    if (tod.freq > pd.offsets.Day(1)) | (tod.freq < pd.offsets.Second(1)):
+        raise ValueError(
+            """
+            Transformer intended to be used with input frequency of greater than
+            or equal to one day and with a frequency of less or equal to than
+            1 second. Contributions welcome on adapting for these use cases.
+            """
+        )
+    return freq_ind
diff --git a/aeon/transformations/tests/test_clear_sky.py b/aeon/transformations/series/tests/test_clear_sky.py
similarity index 90%
rename from aeon/transformations/tests/test_clear_sky.py
rename to aeon/transformations/series/tests/test_clear_sky.py
index 05fff03a90..941b5b4708 100644
--- a/aeon/transformations/tests/test_clear_sky.py
+++ b/aeon/transformations/series/tests/test_clear_sky.py
@@ -7,7 +7,7 @@
 import pytest
 
 from aeon.datasets import load_solar
-from aeon.transformations.clear_sky import ClearSky
+from aeon.transformations.series import ClearSkyTransformer
 from aeon.utils.validation._dependencies import _check_soft_dependencies
 
 output_chk = [0.0, 0.0, 0.901, 0.739, 0.618, 0.0]
@@ -22,7 +22,7 @@ def test_clearsky_trafo_vals():
     y = load_solar(api_version=None)
     # only take every 4H for quickness
     y = y.asfreq("4H")
-    cs_model = ClearSky()
+    cs_model = ClearSkyTransformer()
     y_trafo = cs_model.fit_transform(y)
 
     msg = "ClearSky not transforming values consistently with stored values."
@@ -30,7 +30,7 @@ def test_clearsky_trafo_vals():
 
     y_missing = y.copy()
     y_missing.iloc[6:12] = np.nan
-    cs_model_missing = ClearSky()
+    cs_model_missing = ClearSkyTransformer()
     y_trafo_missing = cs_model_missing.fit_transform(y_missing)
 
     msg = "ClearSky transformer not returning correct number of values."
@@ -39,7 +39,7 @@ def test_clearsky_trafo_vals():
 
     y_period = y.copy()
     y_period.index = y_period.index.to_period()
-    cs_model_period = ClearSky()
+    cs_model_period = ClearSkyTransformer()
     y_trafo_period = cs_model_period.fit_transform(y_period)
 
     msg = "PeriodIndex and DatetimeIndex returning different values"
@@ -56,7 +56,7 @@ def test_clearsky_trafo_range_exception():
 
     # range index should not work
     y = y.reset_index(drop=True)
-    cs_model = ClearSky()
+    cs_model = ClearSkyTransformer()
     with pytest.raises(ValueError):
         cs_model.fit_transform(y)
 
@@ -71,7 +71,7 @@ def test_clearsky_trafo_nofreq_exception():
 
     # no set or inferrable frequency should not work
     y = y.drop(pd.to_datetime("2021-05-01 00:30:00", utc=True))
-    cs_model = ClearSky()
+    cs_model = ClearSkyTransformer()
     with pytest.raises(ValueError):
         cs_model.fit_transform(y)
 
@@ -86,6 +86,6 @@ def test_clearsky_trafo_grdaily_exception():
 
     # gr daily frequency should not work
     y = y.asfreq("2D")
-    cs_model = ClearSky()
+    cs_model = ClearSkyTransformer()
     with pytest.raises(ValueError):
         cs_model.fit_transform(y)
diff --git a/aeon/transformations/subset.py b/aeon/transformations/subset.py
index 1d49ce60cc..397f9ced29 100644
--- a/aeon/transformations/subset.py
+++ b/aeon/transformations/subset.py
@@ -12,7 +12,7 @@
 # TODO: remove in v0.10.0
 @deprecated(
     version="0.9.0",
-    reason="IndexSubset will be removed in version 0.10.",
+    reason="IndexSubset will be removed in version 0.10.0.",
     category=FutureWarning,
 )
 class IndexSubset(BaseTransformer):
@@ -118,7 +118,7 @@ def get_test_params(cls, parameter_set="default"):
 # TODO: remove in v0.10.0
 @deprecated(
     version="0.9.0",
-    reason="ColumnSelect will be removed in version 0.10.",
+    reason="ColumnSelect will be removed in version 0.10.0.",
     category=FutureWarning,
 )
 class ColumnSelect(BaseTransformer):