Assessing time series models with traditional interpretation methods
A time series regressor model can be evaluated as you would evaluate any regression model; that is, using metrics derived from the mean squared error or the R-squared score. There are, of course, cases in which you will need to use a metric with medians, logs, deviances, or absolute values. These models don’t require any of this.
Using standard regression metrics
The evaluate_reg_mdl function can evaluate the model, output some standard regression metrics, and plot them. The parameters for this model are the fitted model (lstm_traffic_mdl), X_train (gen_train), X_test (gen_test), y_train, and y_test.
Optionally, we can specify a y_scaler so that the model is evaluated with the labels’ inverse transformed, which makes the plot and root mean square error (RMSE) much easier to interpret. Another optional parameter that is very much necessary, in this case, is y_truncate=True because our y_train...
