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I want to calculate auto-covariance of 3 arrays X1, X2 and Y which are all stationary random process. Is there any function in sciPy or other library can solve this problem?
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In terms of δ[k] , the autocovariance function is simply CZ[m,n]=σ2δ[m−n].
The covariance may be computed using the Numpy function np. cov() . For example, we have two sets of data x and y , np. cov(x, y) returns a 2D array where entries [0,1] and [1,0] are the covariances.
Method 3: Using plot_acf() A plot of the autocorrelation of a time series by lag is called the AutoCorrelation Function (ACF). Such a plot is also called a correlogram. A correlogram plots the correlation of all possible timesteps. The lagged variables with the highest correlation can be considered for modeling.
In probability theory and statistics, given a stochastic process, the autocovariance is a function that gives the covariance of the process with itself at pairs of time points. Autocovariance is closely related to the autocorrelation of the process in question.
Statsmodels has auto- and cross covariance functions
http://statsmodels.sourceforge.net/devel/generated/statsmodels.tsa.stattools.acovf.html http://statsmodels.sourceforge.net/devel/generated/statsmodels.tsa.stattools.ccovf.html
plus the correlation functions and partial autocorrelation http://statsmodels.sourceforge.net/devel/tsa.html#descriptive-statistics-and-tests
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