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When the predictors are known into the future (e g , calendar-related variables such as time, day-of-week, etc.. ), this is straightforward But when the predictors are themselves unknown, we must either model them separately, or use assumed future values for each predictor.. As with ordinary regression models, in order to obtain forecasts we first need to forecast the predictors.

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To forecast using a regression model with ARIMA errors, we need to forecast the regression part of the model and the ARIMA part of the model, and combine the results.

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