mvgam (version 1.1.4)

ensemble.mvgam_forecast: Combine forecasts from mvgam models into evenly weighted ensembles

Description

Generate evenly weighted ensemble forecast distributions from mvgam_forecast objects

Usage

ensemble(object, ...)
# S3 method for mvgam_forecast
ensemble(object, ..., ndraws = 5000)

Value

An object of class mvgam_forecast containing the ensemble predictions. This object can be readily used with the supplied S3 functions plot and score

Arguments

object

list object of class mvgam_forecast. See forecast.mvgam()

...

More mvgam_forecast objects.

ndraws

Positive integer specifying the number of draws to use from each forecast distribution for creating the ensemble. If some of the ensemble members have fewer draws than ndraws, their forecast distributions will be resampled with replacement to achieve the correct number of draws

Author

Nicholas J Clark

Details

It is widely recognised in the forecasting literature that combining forecasts from different models often results in improved forecast accuracy. The simplest way to create an ensemble is to use evenly weighted combinations of forecasts from the different models. This is straightforward to do in a Bayesian setting with mvgam as the posterior MCMC draws contained in each mvgam_forecast object will already implicitly capture correlations among the temporal posterior predictions.

See Also

plot.mvgam_forecast, score.mvgam_forecast

Examples

# \donttest{
# Simulate some series and fit a few competing dynamic models
set.seed(1)
simdat <- sim_mvgam(n_series = 1,
                    prop_trend = 0.6,
                    mu = 1)

plot_mvgam_series(data = simdat$data_train,
                 newdata = simdat$data_test)

m1 <- mvgam(y ~ 1,
            trend_formula = ~ time +
              s(season, bs = 'cc', k = 9),
            trend_model = AR(p = 1),
            noncentred = TRUE,
            data = simdat$data_train,
            newdata = simdat$data_test,
            chains = 2,
            silent = 2)

m2 <- mvgam(y ~ time,
            trend_model = RW(),
            noncentred = TRUE,
            data = simdat$data_train,
            newdata = simdat$data_test,
            chains = 2,
            silent = 2)

# Calculate forecast distributions for each model
fc1 <- forecast(m1)
fc2 <- forecast(m2)

# Generate the ensemble forecast
ensemble_fc <- ensemble(fc1, fc2)

# Plot forecasts
plot(fc1)
plot(fc2)
plot(ensemble_fc)

# Score forecasts
score(fc1)
score(fc2)
score(ensemble_fc)
# }