Evaluating forecast models

• It is common practice to separate the data into two portions, training and test data.
• The training data is used to estimate any parameters of a forecasting method and the test data is used to evaluate its accuracy
• Since the test data is not used in determining the forecasts, it should provide a reliable indication of how well the model is likely to forecast on new data.

The size of the test set is typically about 20% of the total sample, although this value depends on how long the sample is and how far ahead you want to forecast.

The test set should ideally be at least as large as the maximum forecast horizon required.

The ModelSelection.TimeSeriesCV provides a scikit-learn compatible interface for cross-validation of time series.