Chapter Summary:
In summary, we suggest that time series forecasting has two phases; in the first phase, a forecaster needs to know the forecasting objective and loss function, select the data set, econometric methodology and the finalize a model based on the controlled forecasting experiment. In the second phase, a forecaster should continuously monitor and maintain forecasting performance of the models. When the model breaks down, as it eventually will, the forecaster must construct a new model following the approach adopted in the first phase. Because one model specification will not remain accurate forever, and even the best model specification may need to be revised at some point, time series forecasting is an evolving process.