In time series analysis, 'q' represents the order of the moving average component in ARIMA models, specifically indicating how many lagged forecast errors are included in the model. This parameter plays a crucial role in capturing the relationship between the current observation and past forecast errors, making it essential for accurately modeling and forecasting time series data. Understanding 'q' helps in defining the structure of both seasonal differencing and integrated models, as it directly influences how past information is utilized to improve predictions.
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