- ccf(x, y, lags=365, bin_method='rectangle', bin_width=0.5, max_gap=inf, min_obs=20, full_output=False, alpha=0.05)#
Method to compute the cross-correlation for irregular time series.
x (pandas.Series) – Pandas Series containing the values to calculate the cross-correlation on. The index has to be a Pandas.DatetimeIndex.
y (pandas.Series) – Pandas Series containing the values to calculate the cross-correlation on. The index has to be a Pandas.DatetimeIndex.
lags (array_like, optional) – numpy array containing the lags in days for which the cross-correlation is calculated. Defaults is all lags from 1 to 365 days.
bin_method (str, optional) – method to determine the type of bin. Options are “rectangle” (default), “gaussian” and “regular” (for regular timesteps).
bin_width (float, optional) – number of days used as the width for the bin to calculate the correlation.
max_gap (float, optional) – Maximum timestep gap in the data. All timesteps above this gap value are not used for calculating the average timestep. This can be helpful when there is a large gap in the data that influences the average timestep.
min_obs (int, optional) – Minimum number of observations in a bin to determine the correlation.
full_output (bool, optional) – If True, also estimated uncertainties are returned. Default is False.
alpha (float) – alpha level to compute the confidence interval (e.g., 1-alpha).
result – If full_output=True, a DataFrame with columns “ccf”, “conf”, and “n”, containning the cross-correlation function, confidence intervals (depends on alpha), and the number of samples n used to compute these, respectively. If full_output=False, only the CCF is returned.
- Return type
>>> ccf = ps.stats.ccf(x, y, bin_method="gaussian")