Model identification is the process of determining which statistical model is appropriate for a given time series data set, ensuring that the selected model can accurately capture the underlying patterns and structures present in the data. This process involves assessing different potential models, particularly in the context of ARIMA, to select the optimal one based on criteria such as fit and predictive power. Accurate model identification is essential for effective forecasting and understanding the dynamics of the time series.
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