Serial correlation, also known as autocorrelation, refers to the correlation of a time series with its own past values. It is crucial in identifying patterns and dependencies within data over time, as it indicates whether past values influence current values. Understanding serial correlation is essential for analyzing time-dependent data, particularly when estimating parameters and making predictions.
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