Evaluate different time series' models.
- Time series: Time series as output by As Timeseries widget.
- Time series model(s): The time series model(s) to evaluate (e.g. VAR or ARIMA).
Evaluate different time series' models. by comparing the errors they make in terms of: root mean squared error (RMSE), median absolute error (MAE), mean absolute percent error (MAPE), prediction of change in direction (POCID), coefficient of determination (R²), Akaike information criterion (AIC), and Bayesian information criterion (BIC).
- Number of folds for time series cross-validation.
- Number of forecast steps to produce in each fold.
- Results for various error measures and information criteria on cross-validated and in-sample data.
In-sample errors are the errors calculated on the training data itself. A stable model is one where in-sample errors and out-of-sample errors don't differ significantly.