# pmdarima.utils.pacf¶

pmdarima.utils.pacf(x, nlags=40, method='ywunbiased', alpha=None)[source][source]

Partial autocorrelation estimated

Parameters: x : 1d array observations of time series for which pacf is calculated nlags : int largest lag for which pacf is returned method : {‘ywunbiased’, ‘ywmle’, ‘ols’} specifies which method for the calculations to use: yw or ywunbiased : 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 alpha : float, optional If a number is given, the confidence intervals for the given level are returned. For instance if alpha=.05, 95 % confidence intervals are returned where the standard deviation is computed according to 1/sqrt(len(x)) pacf : 1d array partial autocorrelations, nlags elements, including lag zero confint : array, optional Confidence intervals for the PACF. Returned if confint is not None.

Notes

This solves yule_walker equations or ols for each desired lag and contains currently duplicate calculations.