statsmodels.graphics.tsaplots.plot_pacf

statsmodels.graphics.tsaplots.plot_pacf(x, ax=None, lags=None, alpha=0.05, method='ywunbiased', use_vlines=True, title='Partial Autocorrelation', zero=True, vlines_kwargs=None, **kwargs)[source]

Plot the partial autocorrelation function

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 (int or array_like, optional) – int or Array of lag values, used on horizontal axis. Uses np.arange(lags) when lags is an int. If not provided, lags=np.arange(len(corr)) is used.

  • 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))

  • 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

  • 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.

  • title (str, optional) – Title to place on plot. Default is ‘Partial Autocorrelation’

  • zero (bool, optional) – Flag indicating whether to include the 0-lag autocorrelation. Default is True.

  • vlines_kwargs (dict, optional) – Optional dictionary of keyword arguments that are passed to vlines.

  • **kwargs (kwargs, optional) – Optional keyword arguments that are directly passed on to the Matplotlib plot and axhline functions.

Returns

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

Return type

Matplotlib figure instance

See also

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

Notes

Plots lags on the horizontal and the correlations on vertical axis. Adapted from matplotlib’s xcorr.

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

kwargs is used to pass matplotlib optional arguments to both the line tracing the autocorrelations and for the horizontal line at 0. These options must be valid for a Line2D object.

vlines_kwargs is used to pass additional optional arguments to the vertical lines connecting each autocorrelation to the axis. These options must be valid for a LineCollection object.

Examples

>>> import pandas as pd
>>> import matplotlib.pyplot as plt
>>> import statsmodels.api as sm
>>> dta = sm.datasets.sunspots.load_pandas().data
>>> dta.index = pd.Index(sm.tsa.datetools.dates_from_range('1700', '2008'))
>>> del dta["YEAR"]
>>> sm.graphics.tsa.plot_acf(dta.values.squeeze(), lags=40)
>>> plt.show()

(Source code, png, hires.png, pdf)

../_images/graphics_tsa_plot_pacf.png