What does the term "autocorrelation" refer to in time series analysis?

In time series analysis, autocorrelation refers specifically to the correlation between a time series and its own past values. This concept is fundamental because it allows analysts to understand how current values of a series are influenced by its previous values. For instance, if a time series exhibits a positive autocorrelation, it means that an increase in the value at one time point is likely associated with increases in values at previous time points.

Identifying autocorrelation is essential in modeling time-dependent data, as it can inform the choice of appropriate models, such as ARIMA (AutoRegressive Integrated Moving Average), which explicitly accounts for these relationships between past and present values. By understanding the autocorrelation structure of a time series, analysts can better forecast future values and make informed decisions based on historical data patterns.