Choosing an algorithm and configuring the training parameters
In Chapter 4, Training a Predictor with AutoML, we let Amazon Forecast make all the choices for us and left all the parameters at their default values, including the choice of algorithm. When you follow this path, Amazon Forecast applies every algorithm it knows on your dataset and selects the winning one by looking at which one achieves the best average weighted absolute percentage error (WAPE) metric in your backtest window (if you kept the default choice for the optimization metric to be used).
At the time of writing this chapter, Amazon Forecast knows about six algorithms. The AutoML process is great when you don't have a precise idea about the algorithm that will give the best result with your dataset. The AutoPredictor
settings also give you the flexibility to experiment easily with an ensembling technique that will let Amazon Forecast devise the best combination of algorithms for each time series of your dataset...