statsmodels.graphics.tsaplots.plot_accf_grid

statsmodels.graphics.tsaplots.plot_accf_grid(x, *, varnames=None, fig=None, lags=None, negative_lags=True, alpha=0.05, use_vlines=True, adjusted=False, fft=False, missing='none', zero=True, auto_ylims=False, bartlett_confint=False, vlines_kwargs=None, **kwargs)[source]

Plot auto/cross-correlation grid

Plots lags on the horizontal axis and the correlations on the vertical axis of each graph.

Parameters:

  • x (array_like) – 2D array of time-series values: rows are observations, columns are variables.

  • varnames (sequence of str, optional) – Variable names to use in plot titles. If x is a pandas dataframe and varnames is provided, it overrides the column names of the dataframe. If varnames is not provided and x is not a dataframe, variable names x[0], x[1], etc. are generated.

  • fig (Matplotlib figure instance, optional) – If given, this figure is used to plot in, otherwise a new figure is created.

  • lags ({int, array_like}, optional) – An int or array of lag values, used on horizontal axes. Uses np.arange(lags) when lags is an int. If not provided, lags=np.arange(len(corr)) is used.

  • negative_lags (bool, optional) – If True, negative lags are shown on the horizontal axes of plots below the main diagonal.

  • alpha (scalar, optional) – If a number is given, the confidence intervals for the given level are plotted, e.g. if alpha=.05, 95 % confidence intervals are shown. If None, confidence intervals are not shown on the plot.

  • use_vlines (bool, optional) – If True, shows vertical lines and markers for the correlation values. If False, only shows markers. The default marker is ‘o’; it can be overridden with a marker kwarg.

  • adjusted (bool) – If True, then denominators for correlations are n-k, otherwise n.

  • fft (bool, optional) – If True, computes the ACF via FFT.

  • missing (str, optional) – A string in [‘none’, ‘raise’, ‘conservative’, ‘drop’] specifying how NaNs are to be treated.

  • zero (bool, optional) – Flag indicating whether to include the 0-lag autocorrelations (which are always equal to 1). Default is True.

  • auto_ylims (bool, optional) – If True, adjusts automatically the vertical axis limits to correlation values.

  • bartlett_confint (bool, default False) – If True, use Bartlett’s formula to calculate confidence intervals in auto-correlation plots. See the description of plot_acf for details. This argument does not affect cross-correlation plots.

  • 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:

If fig is None, the created figure. Otherwise, fig is returned. Plots on the grid show the cross-correlation of the row variable with the lags of the column variable.

Return type:

Figure

See also

statsmodels.graphics.tsaplots

Examples

>>> import pandas as pd
>>> import matplotlib.pyplot as plt
>>> import statsmodels.api as sm

>>> dta = sm.datasets.macrodata.load_pandas().data
>>> diffed = dta.diff().dropna()
>>> sm.graphics.tsa.plot_accf_grid(diffed[["unemp", "infl"]])
>>> plt.show()