Use of auto-correlations in identifying time series 

Auto-correlation is a statistical concept that measures the linear relationship between observations in a time series data set at different time lags. It is a useful tool for analyzing and identifying patterns in time series data. By examining the auto-correlation function (ACF) plot, we can gain insights into the underlying structure and behavior of the time series.

Here are some ways auto-correlations can be used in identifying time series characteristics:

1. Stationarity: Auto-correlation can help determine the stationarity of a time series. Stationarity refers to the property where the statistical properties of a time series, such as ______ ___ ________ ________ ___ ___ _______.
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