legion: Forecasting Using Multivariate Models

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;