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:

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

`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:

`print(ourModel)`

– function prints brief output with explanation of what was fitted, with what parameters, errors etc;`summary(ourModel)`

– alias for`print(ourModel)`

;`actuals(ourModel)`

– returns actual values;`fitted(ourModel)`

– fitted values of the model;`residuals(ourModel)`

– residuals of constructed model;`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;`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.`forecast(ourModel)`

– point and interval forecasts;`plot(forecast(ourModel))`

– produces graph with actuals, forecast, fitted and prediction interval using`graphmaker()`

function from`greybox`

package.`simulate(ourModel)`

– produces data simulated from provided model. Only works for`ves()`

for now;`logLik(ourModel)`

– returns log-likelihood of the model;`nobs(ourModel)`

– returns number of observations in-sample we had;`nparam(ourModel)`

– number of estimated parameters (originally from`greybox`

package);`nvariate(ourModel)`

– number of variates, time series in the model (originally from`greybox`

package);`sigma(ourModel)`

– covariance matrix of the residuals of the model;`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;`errorType(ourModel)`

– the type of the error of a model (additive or multiplicative);`coef(ourModel)`

– returns the vector of all the estimated coefficients of the model;