plot_correlations#

plot_correlations(series, lags=24, alpha=0.05, zero_lag=True, acf_fft=False, acf_adjusted=True, pacf_method='ywadjusted', suptitle=None, series_title=None, acf_title='Autocorrelation', pacf_title='Partial Autocorrelation')[source]#

Plot series and its ACF and PACF values.

Parameters:

seriespd.Series: A time series.
lagsint, default = 24: Number of lags to include in ACF and PACF plots
alphaint, default = 0.05: Alpha value used to set confidence intervals. Alpha = 0.05 results in 95% confidence interval with standard deviation calculated via Bartlett’s formula.
zero_lagbool, default = True: If True, start ACF and PACF plots at 0th lag
acf_fftbool, = False: Whether to compute ACF via FFT.
acf_adjustedbool, default = True: If True, denominator of ACF calculations uses n-k instead of n, where n is number of observations and k is the lag.
pacf_methodstr, default = ‘ywadjusted’: Method to use in calculation of PACF.
suptitlestr, default = None: The text to use as the Figure’s suptitle.
series_titlestr, default = None: Used to set the title of the series plot if provided. Otherwise, series plot has no title.
acf_titlestr, default = ‘Autocorrelation’: Used to set title of ACF plot.
pacf_titlestr, default = ‘Partial Autocorrelation’: Used to set title of PACF plot.

Returns:

figmatplotlib.figure.Figure
axesnp.ndarray: Array of the figure’s Axe objects

Examples

>>> from sktime.utils.plotting import plot_correlations
>>> from sktime.datasets import load_airline
>>> y = load_airline()
>>> fig, ax = plot_correlations(y)  