How to use the tsfresh.feature_extraction.feature_calculators function in tsfresh

def time_series_has_duplicate_min(x):
    """
    Checks if the minimal value of x is observed more than once

    :param x: the time series to calculate the feature of
    :type x: pandas.Series
    :return: the value of this feature
    :return type: bool
    """
    return ts_feature_calculators.has_duplicate_min(x)

def time_series_skewness(x):
    """
    Returns the sample skewness of x (calculated with the adjusted Fisher-Pearson standardized
    moment coefficient G1).

    :param x: the time series to calculate the feature of
    :type x: pandas.Series
    :return: the value of this feature
    :return type: float
    """
    return ts_feature_calculators.skewness(x)

def time_series_standard_deviation(x):
    """
    Returns the standard deviation of x

    :param x: the time series to calculate the feature of
    :type x: pandas.Series
    :return: the value of this feature
    :return type: float
    """
    return ts_feature_calculators.standard_deviation(x)

predict :math:`x_t` whereas in (b), future values are used to calculate the past value :math:`x_{t-k}`.\
        It is said in :cite:`Wilson2015` that "for an AR(p), the partial autocorrelations [ :math:`\\alpha_k` ] \
        will be nonzero for `k<=p` and zero for `k>p`."\

        With this property, it is used to determine the lag of an AR-Process.

        :param x: the time series to calculate the feature of
        :type x: pandas.Series
        :param param: contains dictionaries {"lag": val} with int val indicating the lag to be returned
        :type param: list
        :return: the value of this feature
        :rtype: float
        """
        if param is None:
            param = [{'lag': 3}, {'lag': 5}, {'lag': 6}]
        _partialc = feature_calculators.partial_autocorrelation(x, param)
        logging.debug("partial autocorrelation by tsfresh calculated")
        return _partialc

def time_series_has_duplicate(x):
    """
    Checks if any value in x occurs more than once

    :param x: the time series to calculate the feature of
    :type x: pandas.Series
    :return: the value of this feature
    :return type: bool
    """
    return ts_feature_calculators.has_duplicate(x)