statsmodels.graphics.tsaplots.plot_acf

statsmodels.graphics.tsaplots.plot_acf(x, ax=None, lags=None, alpha=0.05, use_vlines=True, unbiased=False, fft=False, title='Autocorrelation', zero=True, vlines_kwargs=None, **kwargs)

Plot the autocorrelation function

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

Parameters

xarray_like

Array of time-series values

axMatplotlib AxesSubplot instance, optional

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

lagsint 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.

alphascalar, 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 Bartlett’s formula. If None, no confidence intervals are plotted.

use_vlinesbool, 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.

unbiasedbool

If True, then denominators for autocovariance are n-k, otherwise n

fftbool, optional

If True, computes the ACF via FFT.

titlestr, optional

Title to place on plot. Default is ‘Autocorrelation’

zerobool, optional

Flag indicating whether to include the 0-lag autocorrelation. Default is True.

vlines_kwargsdict, optional

Optional dictionary of keyword arguments that are passed to vlines.

**kwargskwargs, optional

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

Returns

figMatplotlib 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

Notes

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()

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