Autocorrelation
One of the most distinctive characteristics of a time series is the mutual dependence between the observations. In fact, it’s important in Time Series Analysis to focus on the relationship between the lagged values of a time series.
In bivariate statistics, to analyze the relationship between two numerical variables, the Pearson linear correlation index is probably the most used (and abused) association metric. The correlation index is a relative (symmetric) measure of the linear relationship existing between two quantitative variables, X and Y, and it’s calculated as follows:
Where:
is the sample covariance between X and Y
.
and 
are the sample standard deviation of X and Y.
Just as the correlation index measures the linear relationship between two variables X, the autocorrelation index measures the linear relationship between the lagged values of a time series. As the standard deviation and the mean...
