Autocorrelation plot using Matplotlib

Last Updated : 6 Aug, 2021

Autocorrelation plots are a commonly used tool for checking randomness in a data set. This randomness is ascertained by computing autocorrelations for data values at varying time lags.

Characteristics Of Autocorrelation Plot :

It measures a set of current values against a set of past values and finds whether they correlate.
It is the correlation of one-time series data to another time series data which has a time lag.
It varies from +1 to -1.
An autocorrelation of +1 indicates that if time series one increases in value the time series 2 also increases in proportion to the change in time series 1.
An autocorrelation of -1 indicates that if time series one increases in value the time series 2 decreases in proportion to the change in time series 1.

Application of Autocorrelation:

Pattern recognition.
Signal detection.
Signal processing.
Estimating pitch.
Technical analysis of stocks.

Plotting the Autocorrelation Plot

To plot the Autocorrelation Plot we can use matplotlib and plot it easily by using matplotlib.pyplot.acorr() function.

Syntax: matplotlib.pyplot.acorr(x, *, data=None, **kwargs)
Parameters:

'x' : This parameter is a sequence of scalar.
'detrend' : This parameter is an optional parameter. Its default value is mlab.detrend_none.
'normed' : This parameter is also an optional parameter and contains the bool value. Its default value is True.
'usevlines' : This parameter is also an optional parameter and contains the bool value. Its default value is True.
'maxlags' : This parameter is also an optional parameter and contains the integer value. Its default value is 10.
'linestyle' : This parameter is also an optional parameter and used for plotting the data points, only when usevlines is False.
'marker' : This parameter is also an optional parameter and contains the string. Its default value is ‘o’.

Returns: (lags, c, line, b)
Where:

lags are a length 2`maxlags+1 lag vector.
c is the 2`maxlags+1 auto correlation vector.
line is a Line2D instance returned by plot.
b is the x-axis.