pastas.stats.core.acf
=====================

.. py:function:: pastas.stats.core.acf(x: pandas.Series, lags: pastas.typing.ArrayLike = 365, bin_method: str = 'regular', bin_width: float = 0.5, max_gap: float = inf, min_obs: int = 50, full_output: bool = False, alpha: float = 0.05, fallback_bin_method: str = 'gaussian') -> pandas.Series | pandas.DataFrame

   
   Calculate the autocorrelation function for irregular time steps.

   :param x: Pandas Series containing the values to calculate the cross-correlation on.
             The index has to be a Pandas.DatetimeIndex.
   :type x: pandas.Series
   :param lags: numpy array containing the lags in days for which the cross-correlation if
                calculated. Defaults is all lags from 1 to 365 days.
   :type lags: array_like, optional
   :param bin_method: method to determine the type of bin. Options are "regular" for regular data
                      (default), and "gaussian" and "rectangle" for irregular data.
   :type bin_method: str, optional
   :param bin_width: number of days used as the width for the bin to calculate the correlation.
   :type bin_width: float, optional
   :param max_gap: Maximum time step gap in the data. All time steps above this gap value are
                   not used for calculating the average time step. This can be helpful when
                   there is a large gap in the data that influences the average time step.
   :type max_gap: float, optional
   :param min_obs: Minimum number of observations in a bin to determine the correlation.
   :type min_obs: int, optional
   :param full_output: If True, also estimated uncertainties are returned. Default is False.
   :type full_output: bool, optional
   :param alpha: alpha level to compute the confidence interval (e.g., 1-alpha).
   :type alpha: float, optional

   :returns: **result** -- If full_output=True, a DataFrame with columns "acf", "conf", and "n",
             containing the autocorrelation function, confidence intervals (depends on
             alpha), and the number of samples n used to compute these, respectively. If
             full_output=False, only the ACF is returned.
   :rtype: pandas.Series or pandas.DataFrame

   .. rubric:: Notes

   The ACF method primarily tries to estimate the autocorrelation using common
   techniques if the time step between the measurements is regular. If the time step
   is irregular, the method falls back to an alternative method to calculate the
   autocorrelation function for irregular timesteps based on the slotting technique
   :cite:t:`rehfeld_comparison_2011`. Different methods (kernels) to bin the data are
   available.

   Estimating the autocorrelation for irregular time steps can be challenging.
   Depending on the data and the binning method and settings used, the correlation can
   be above 1 or below -1. If this occurs, a warning is raised.

   .. rubric:: Examples

   For example, to estimate the autocorrelation for every second lag up to lags of
   one year:

   >>> acf = ps.stats.acf(x, lags=np.arange(1.0, 366.0, 2.0))

   .. seealso:: :py:obj:`pastas.stats.ccf`, :py:obj:`statsmodels.api.tsa.acf`















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