Chapter 5: Customizing Your Predictor Training
In the previous chapter, you trained your first predictor on a household energy consumption dataset. You used the fully automated machine learning (AutoML) approach offered by default by Amazon Forecast, which let you obtain an accurate forecast without any ML or statistical knowledge about time series forecasting.
In this chapter, you will continue to work on the same datasets, but you will explore the flexibility that Amazon Forecast gives you when training a predictor. This will allow you to better understand when and how you can adjust your forecasting approach based on specificities in your dataset or specific domain knowledge you wish to leverage.
In this chapter, we're going to cover the following main topics:
- Choosing an algorithm and configuring the training parameters
- Leveraging hyperparameter optimization (HPO)
- Reinforcing your backtesting strategy
- Including holiday and weather data
- Implementing...