When using this function, the analyst evaluates the same time series and compares it with its initial value over one or more periods. The ACF is a function of the lag τ, which determines the change in time pattern to estimate the similarity between two or more data points. In simple terms, the ACF allows an analyst to compare the present value of a data set with its past value and to evaluate the similarity between the two variables. It helps to determine how patterns in a time series correlate when analyzed with past versions of themselves. The autocorrelation function is a statistical indicator used to measure and compare the degree of similarity between a time series and an initial version of itself.
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