Now that the model has been trained, tested, tuned (if required), and evaluated successfully, we can move forward to the last step and finalize the forecast. This step is based on recalibrating the model's weights or coefficients with the full series. There are two approaches to using the model parameter setting:
- If the model was tuned manually, you should use the exact tuning parameters that were used on the trained model
- If the model was tuned automatically by an algorithm (such as the auto.arima function we used previously), you can do either of the following:
- Extract the parameter setting that was used by with the training partition
- Let the algorithm retune the model parameters using the full series, under the assumption that the algorithm has the ability to adjust the model parameters correctly when training the model with new data
The use...
