legion: Forecasting Using Multivariate Models

In legion: Forecasting Using Multivariate Models

knitr::opts_chunk$set(fig.width=6, fig.height=4, fig.path='Figs/', fig.show='hold',
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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;

legion documentation built on Feb. 16, 2023, 5:34 p.m.