Summary
In this chapter, you learned how to generate, export, and visualize the predictions for the models you trained in the previous chapters. You also learned how you could use the forecast export features to monitor the performance of your predictions at the time series level (and not only at the aggregated level as provided by the predictor evaluation dashboard).
Generating forecasts will be your key activity once a model is trained. It's important that you practice this step, which includes establishing a sound error analysis process. Using error analysis at the item level is indeed critical to understand the areas where your forecasts can be improved and where a significant change in your data distribution impacts its performance.
In the next chapter, you will set up fully automated solutions that will perform even more heavy lifting for you so that you can focus on improving your forecast accuracy over time. You will also have a look at how you can put together the...