pandas.plotting.autocorrelation_plot — pandas 3.0.0rc0+52.gb43b95d2b4 documentation (original) (raw)

pandas.plotting.autocorrelation_plot(series, ax=None, **kwargs)[source]#

Autocorrelation plot for time series.

This method generates an autocorrelation plot for a given time series, which helps to identify any periodic structure or correlation within the data across various lags. It shows the correlation of a time series with a delayed copy of itself as a function of delay. Autocorrelation plots are useful for checking randomness in a data set. If the data are random, the autocorrelations should be near zero for any and all time-lag separations. If the data are not random, then one or more of the autocorrelations will be significantly non-zero.

Parameters:

seriesSeries

The time series to visualize.

axMatplotlib axis object, optional

The matplotlib axis object to use.

**kwargs

Options to pass to matplotlib plotting method.

Returns:

matplotlib.axes.Axes

The matplotlib axes containing the autocorrelation plot.

See also

Series.autocorr

Compute the lag-N autocorrelation for a Series.

plotting.lag_plot

Lag plot for time series.

Examples

The horizontal lines in the plot correspond to 95% and 99% confidence bands.

The dashed line is 99% confidence band.

spacing = np.linspace(-9 * np.pi, 9 * np.pi, num=1000) s = pd.Series(0.7 * np.random.rand(1000) + 0.3 * np.sin(spacing)) pd.plotting.autocorrelation_plot(s)