statsmodels.graphics.tsaplots.plot_pacf

statsmodels.graphics.tsaplots.plot_pacf(x, ax=None, lags=None, alpha=0.05, method='ywm', use_vlines=True, **kwargs)[source]

Plot the partial autocorrelation function

Plots lags on the horizontal and the correlations on vertical axis.

Parameters:

x : array_like

Array of time-series values

ax : Matplotlib AxesSubplot instance, optional

If given, this subplot is used to plot in instead of a new figure being created.

lags : array_like, optional

Array of lag values, used on horizontal axis. If not given, lags=np.arange(len(corr)) is used.

alpha : scalar, 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))

method : ‘ywunbiased’ (default) or ‘ywmle’ or ‘ols’

specifies which method for the calculations to use:

  • yw or ywunbiased : yule walker with bias correction in denominator for acovf
  • 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

use_vlines : bool, optional

If True, vertical lines and markers are plotted. If False, only markers are plotted. The default marker is ‘o’; it can be overridden with a marker kwarg.

**kwargs : kwargs, optional

Optional keyword arguments that are directly passed on to the Matplotlib plot and axhline functions.
Returns:

fig : Matplotlib figure instance

If ax is None, the created figure. Otherwise the figure to which ax is connected.

See also

matplotlib.pyplot.xcorr, matplotlib.pyplot.acorr, mpl_examples

Notes

Adapted from matplotlib’s xcorr.

Data are plotted as plot(lags, corr, **kwargs)