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, denonimator 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 aeon.visualisation import plot_correlations >>> from aeon.datasets import load_airline >>> y = load_airline(return_array=False) >>> fig, ax = plot_correlations(y)