Model diagnostics refers to the methods used to assess the adequacy and performance of statistical models, particularly in the context of time series analysis. These diagnostics help identify whether a model appropriately captures the underlying data patterns, revealing issues like autocorrelation, non-stationarity, or heteroscedasticity that may affect the model's reliability and predictive power.
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