The theoretical autocorrelation function (ACF) is a statistical tool used to measure the correlation between observations of a time series at different time lags, assuming a specific model structure. It helps in understanding the underlying data patterns by providing insights into how past values of a series relate to its future values. This concept is crucial in model selection and validation, as it allows for the assessment of the temporal dependencies inherent in the data.
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