Variable importance for forecasting model.

Variable importance for forecasting model.

Usage

get_var_imp(object, plot = TRUE)

Arguments

object

A list class of ARml or forecast object derived from ARml

plot

Boolean, if TRUE, variable importance will be ploted.

Value

A list class of "varImp.train". See varImp or a "trellis" plot.

Author

Resul Akay

Examples

train <- window(AirPassengers, end = c(1959, 12))

test <- window(AirPassengers, start = c(1960, 1))

ARml(train, caret_method = "lm", max_lag = 12,
 pre_process = "center") -> fit
#> initial_window = NULL. Setting initial_window = 112
#> + Training112: intercept=TRUE 
#> - Training112: intercept=TRUE 
#> + Training113: intercept=TRUE 
#> - Training113: intercept=TRUE 
#> + Training114: intercept=TRUE 
#> - Training114: intercept=TRUE 
#> + Training115: intercept=TRUE 
#> - Training115: intercept=TRUE 
#> + Training116: intercept=TRUE 
#> - Training116: intercept=TRUE 
#> Aggregating results
#> Fitting final model on full training set
#> Performing horizon-specific calibration for conformal prediction intervals...
#> Calibrating conformal scores using 34 rolling windows...
#> Calibration complete. Samples per horizon: 34 to 34

forecast(fit, h = length(test), level = c(80,95)) -> fc

autoplot(fc)+ autolayer(test)


accuracy(fc, test)
#>                        ME     RMSE       MAE        MPE     MAPE      MASE
#> Training set 3.434088e-15 10.19861  7.884296 -0.1380603 3.263387 0.2589260
#> Test set     5.515070e+00 19.71858 17.108979  0.8260714 3.540353 0.5618712
#>                    ACF1 Theil's U
#> Training set 0.07296876        NA
#> Test set     0.32299513 0.3864957

get_var_imp(fc, plot = TRUE)