Partial autocorrelation measures the relationship between a time series and its own past values, while controlling for the effects of intervening values. This concept helps in identifying the direct influence of earlier observations on a given observation, removing any interference from other lagged values. It's essential for understanding the underlying structure of time series data and aids in model selection when analyzing patterns and relationships.
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