Summary
In this chapter, you discovered the many possibilities Amazon Forecast gives you to customize your predictor training to your datasets. You learned how to choose the best algorithm to fit your problem and how to customize different parameters (quantiles, the missing values' filling strategy, and supplementary features usage) to try to improve your forecasting models.
The AutoML capability of Amazon Forecast is a key differentiator when dealing with a new business case or a new dataset. It gives you good directions and reliable results with a fast turnaround. However, achieving higher accuracy to meet your business needs means that you must sometimes be able to override Amazon Forecast decisions by orienting its choice of algorithms, deciding how to process the features of your dataset, or simply requesting a different set of outputs by selecting forecast types that match the way your decision process is run from a business perspective.
In the next chapter, you will...