Data Science Statistics
Autocorrelation measures the correlation of a time series with its own past values. This concept is crucial for understanding patterns in data that vary over time, helping to identify trends, seasonal effects, or cycles. Recognizing autocorrelation is essential for model diagnostics and assumptions, as it informs analysts whether a time series is stationary and can significantly influence the accuracy of predictions.
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