#!/usr/bin/env python3 -u
# -*- coding: utf-8 -*-
# copyright: sktime developers, BSD-3-Clause License (see LICENSE file)

"""Auto-correlation transformations.

Module :mod:`sktime.transformations.series` implements auto-correlation
transformers.
"""

__author__ = ["<NAME>"]
__all__ = ["AutoCorrelationTransformer", "PartialAutoCorrelationTransformer"]

import pandas as pd
from statsmodels.tsa.stattools import acf
from statsmodels.tsa.stattools import pacf

from sktime.transformations.base import _SeriesToSeriesTransformer
from sktime.utils.validation.series import check_series


class AutoCorrelationTransformer(_SeriesToSeriesTransformer):
    """Auto-correlation transformer.

    Examples
    --------
    >>> from sktime.transformations.series.acf import PartialAutoCorrelationTransformer
    >>> from sklearn.preprocessing import MinMaxScaler
    >>> from sktime.datasets import load_airline
    >>> y = load_airline()
    >>> transformer = AutoCorrelationTransformer(n_lags=12)
    >>> y_hat = transformer.fit_transform(y)
    """

    _tags = {"univariate-only": True, "fit-in-transform": True}

    def __init__(
        self,
        adjusted=False,
        n_lags=None,
        qstat=False,
        fft=False,
        missing="none",
    ):
        self.adjusted = adjusted
        self.n_lags = n_lags
        self.qstat = qstat
        self.fft = fft
        self.missing = missing
        super(AutoCorrelationTransformer, self).__init__()

    def transform(self, Z, X=None):
        """Transform data.

        Parameters
        ----------
        Z : pd.Series
            Series to transform
        X : pd.DataFrame, optional (default=None)
            Exogenous data used in transformation

        Returns
        -------
        Zt : pd.Series
            Transformed series
        """
        self.check_is_fitted()
        z = check_series(Z, enforce_univariate=True)

        # Passing an alpha values other than None would return confidence intervals
        # and break the signature of the series-to-series transformer
        zt = acf(
            z,
            adjusted=self.adjusted,
            nlags=self.n_lags,
            qstat=self.qstat,
            fft=self.fft,
            alpha=None,
            missing=self.missing,
        )
        return pd.Series(zt)


class PartialAutoCorrelationTransformer(_SeriesToSeriesTransformer):
    """Partial auto-correlation transformer.

    Parameters
    ----------
    n_lags : int, optional (default=None)
         largest lag for which pacf is returned
    method : str {'ywadjusted', 'ywmle', 'ols'}
         specifies which method for the calculations to use:
        - yw or ywadjusted : yule walker with bias correction in denominator
          for acovf. Default.
        - ywm or ywmle : yule walker without bias correction
        - ols - regression of time series on lags of it and on constant
        - ld or ldunbiased : Levinson-Durbin recursion with bias correction
        - ldb or ldbiased : Levinson-Durbin recursion without bias correction

    Examples
    --------
    >>> from sktime.transformations.series.acf import AutoCorrelationTransformer
    >>> from sklearn.preprocessing import MinMaxScaler
    >>> from sktime.datasets import load_airline
    >>> y = load_airline()
    >>> transformer = AutoCorrelationTransformer(n_lags=12)
    >>> y_hat = transformer.fit_transform(y)
    """

    _tags = {"univariate-only": True, "fit-in-transform": True}

    def __init__(
        self,
        n_lags=None,
        method="ywadjusted",
    ):
        self.n_lags = n_lags
        self.method = method
        super(PartialAutoCorrelationTransformer, self).__init__()

    def transform(self, Z, X=None):
        """Transform data.

        Parameters
        ----------
        Z : pd.Series
            Series to transform
        X : pd.DataFrame, optional (default=None)
            Exogenous data used in transformation

        Returns
        -------
        Zt : pd.Series
            Transformed series
        """
        self.check_is_fitted()
        z = check_series(Z, enforce_univariate=True)

        # Passing an alpha values other than None would return confidence intervals
        # and break the signature of the series-to-series transformer
        zt = pacf(z, nlags=self.n_lags, method=self.method, alpha=None)
        return pd.Series(zt)

#!/usr/bin/env python3 -u
# -*- coding: utf-8 -*-
# copyright: sktime developers, BSD-3-Clause License (see LICENSE file)

"""Auto-correlation transformations.

Module :mod:`sktime.transformations.series` implements auto-correlation
transformers.
"""

__author__ = ["<NAME>"]
__all__ = ["AutoCorrelationTransformer", "PartialAutoCorrelationTransformer"]

import pandas as pd
from statsmodels.tsa.stattools import acf
from statsmodels.tsa.stattools import pacf

from sktime.transformations.base import _SeriesToSeriesTransformer
from sktime.utils.validation.series import check_series


class AutoCorrelationTransformer(_SeriesToSeriesTransformer):
    """Auto-correlation transformer.

    Examples
    --------
    >>> from sktime.transformations.series.acf import PartialAutoCorrelationTransformer
    >>> from sklearn.preprocessing import MinMaxScaler
    >>> from sktime.datasets import load_airline
    >>> y = load_airline()
    >>> transformer = AutoCorrelationTransformer(n_lags=12)
    >>> y_hat = transformer.fit_transform(y)
    """

    _tags = {"univariate-only": True, "fit-in-transform": True}

    def __init__(
        self,
        adjusted=False,
        n_lags=None,
        qstat=False,
        fft=False,
        missing="none",
    ):
        self.adjusted = adjusted
        self.n_lags = n_lags
        self.qstat = qstat
        self.fft = fft
        self.missing = missing
        super(AutoCorrelationTransformer, self).__init__()

    def transform(self, Z, X=None):
        """Transform data.

        Parameters
        ----------
        Z : pd.Series
            Series to transform
        X : pd.DataFrame, optional (default=None)
            Exogenous data used in transformation

        Returns
        -------
        Zt : pd.Series
            Transformed series
        """
        self.check_is_fitted()
        z = check_series(Z, enforce_univariate=True)

        # Passing an alpha values other than None would return confidence intervals
        # and break the signature of the series-to-series transformer
        zt = acf(
            z,
            adjusted=self.adjusted,
            nlags=self.n_lags,
            qstat=self.qstat,
            fft=self.fft,
            alpha=None,
            missing=self.missing,
        )
        return pd.Series(zt)


class PartialAutoCorrelationTransformer(_SeriesToSeriesTransformer):
    """Partial auto-correlation transformer.

    Parameters
    ----------
    n_lags : int, optional (default=None)
         largest lag for which pacf is returned
    method : str {'ywadjusted', 'ywmle', 'ols'}
         specifies which method for the calculations to use:
        - yw or ywadjusted : yule walker with bias correction in denominator
          for acovf. Default.
        - ywm or ywmle : yule walker without bias correction
        - ols - regression of time series on lags of it and on constant
        - ld or ldunbiased : Levinson-Durbin recursion with bias correction
        - ldb or ldbiased : Levinson-Durbin recursion without bias correction

    Examples
    --------
    >>> from sktime.transformations.series.acf import AutoCorrelationTransformer
    >>> from sklearn.preprocessing import MinMaxScaler
    >>> from sktime.datasets import load_airline
    >>> y = load_airline()
    >>> transformer = AutoCorrelationTransformer(n_lags=12)
    >>> y_hat = transformer.fit_transform(y)
    """

    _tags = {"univariate-only": True, "fit-in-transform": True}

    def __init__(
        self,
        n_lags=None,
        method="ywadjusted",
    ):
        self.n_lags = n_lags
        self.method = method
        super(PartialAutoCorrelationTransformer, self).__init__()

    def transform(self, Z, X=None):
        """Transform data.

        Parameters
        ----------
        Z : pd.Series
            Series to transform
        X : pd.DataFrame, optional (default=None)
            Exogenous data used in transformation

        Returns
        -------
        Zt : pd.Series
            Transformed series
        """
        self.check_is_fitted()
        z = check_series(Z, enforce_univariate=True)

        # Passing an alpha values other than None would return confidence intervals
        # and break the signature of the series-to-series transformer
        zt = pacf(z, nlags=self.n_lags, method=self.method, alpha=None)
        return pd.Series(zt)

#!/usr/bin/env python3 -u # -*- coding: utf-8 -*- # copyright: sktime developers, BSD-3-Clause License (see LICENSE file) Auto-correlation transformations.  Module :mod:`sktime.transformations.series` implements auto-correlation transformers. Auto-correlation transformer.      Examples     --------     >>> from sktime.transformations.series.acf import PartialAutoCorrelationTransformer     >>> from sklearn.preprocessing import MinMaxScaler     >>> from sktime.datasets import load_airline     >>> y = load_airline()     >>> transformer = AutoCorrelationTransformer(n_lags=12)     >>> y_hat = transformer.fit_transform(y) Transform data.          Parameters         ----------         Z : pd.Series             Series to transform         X : pd.DataFrame, optional (default=None)             Exogenous data used in transformation          Returns         -------         Zt : pd.Series             Transformed series # Passing an alpha values other than None would return confidence intervals # and break the signature of the series-to-series transformer Partial auto-correlation transformer.      Parameters     ----------     n_lags : int, optional (default=None)          largest lag for which pacf is returned     method : str {'ywadjusted', 'ywmle', 'ols'}          specifies which method for the calculations to use:         - yw or ywadjusted : yule walker with bias correction in denominator           for acovf. Default.         - ywm or ywmle : yule walker without bias correction         - ols - regression of time series on lags of it and on constant         - ld or ldunbiased : Levinson-Durbin recursion with bias correction         - ldb or ldbiased : Levinson-Durbin recursion without bias correction      Examples     --------     >>> from sktime.transformations.series.acf import AutoCorrelationTransformer     >>> from sklearn.preprocessing import MinMaxScaler     >>> from sktime.datasets import load_airline     >>> y = load_airline()     >>> transformer = AutoCorrelationTransformer(n_lags=12)     >>> y_hat = transformer.fit_transform(y) Transform data.          Parameters         ----------         Z : pd.Series             Series to transform         X : pd.DataFrame, optional (default=None)             Exogenous data used in transformation          Returns         -------         Zt : pd.Series             Transformed series # Passing an alpha values other than None would return confidence intervals # and break the signature of the series-to-series transformer