legion: Forecasting Using Multivariate Models

Ivan Svetunkov


This vignette explains how to use functions in legion package, what they produce, what each field in outputs and what returned values mean.

The package includes the following functions:

  1. ves() - Vector Exponential Smoothing;
  2. vets() - Vector ETS with PIC taxonomy;
  3. oves() - Occurrence part of the vector ETS model.

Methods for the class legion

There are several methods that can be used together with the forecasting functions of the package. When a model is saved to some object ourModel, these function will do some magic. Here’s the list of all the available methods with brief explanations:

  1. print(ourModel) – function prints brief output with explanation of what was fitted, with what parameters, errors etc;
  2. summary(ourModel) – alias for print(ourModel);
  3. actuals(ourModel) – returns actual values;
  4. fitted(ourModel) – fitted values of the model;
  5. residuals(ourModel) – residuals of constructed model;
  6. AIC(ourModel), BIC(ourModel), AICc(ourModel) and BICc(ourModel) – information criteria of the constructed model. AICc() and BICc() functions are not standard stats functions and are imported from greybox package and modified in legion for the specific models;
  7. plot(ourModel) – produces plots for the diagnostics of the constructed model. There are 9 options of what to produce, see ?plot.legion() for more details. Prepare the canvas via par(mfcol=...) before using this function otherwise the plotting might take time.
  8. forecast(ourModel) – point and interval forecasts;
  9. plot(forecast(ourModel)) – produces graph with actuals, forecast, fitted and prediction interval using graphmaker() function from greybox package.
  10. simulate(ourModel) – produces data simulated from provided model. Only works for ves()for now;
  11. logLik(ourModel) – returns log-likelihood of the model;
  12. nobs(ourModel) – returns number of observations in-sample we had;
  13. nparam(ourModel) – number of estimated parameters (originally from greybox package);
  14. nvariate(ourModel) – number of variates, time series in the model (originally from greybox package);
  15. sigma(ourModel) – covariance matrix of the residuals of the model;
  16. modelType(ourModel) – returns the type of the model. Returns something like “MMM” for ETS(MMM). Can be used with ves() and vets(). In the latter case can also accept pic=TRUE, returning the PIC restrictions;
  17. errorType(ourModel) – the type of the error of a model (additive or multiplicative);
  18. coef(ourModel) – returns the vector of all the estimated coefficients of the model;