Rlgt (version 0.2-2)

blgt.multi.forecast: Rlgt LSGT Gibbs run in parallel

Description

Fit a list of series and produce forecast, then calculate the accuracy.

Usage

blgt.multi.forecast(
  train,
  future,
  n.samples = 20000,
  burnin = 10000,
  parallel = T,
  m = 1,
  homoscedastic = F
)

Value

returns a forecast object compatible with the forecast package in R

Arguments

train

A list of training series.

future

A list of corresponding future values of the series.

n.samples

The number of samples to sample from the posterior (the default is 2e4).

burnin

The number of burn in samples (the default is 1e4).

parallel

Whether run in parallel or not (Boolean value only, default TRUE).

m

The seasonality period, with default m=1, i.e., no seasonality specified.

homoscedastic

Run with homoscedastic or heteroscedastic version of the Gibbs sampler version. By default it is set to FALSE, i.e., run a heteroscedastic model.

Examples

if (FALSE) demo(exampleScript)
if (FALSE) {
## Build data and test
library(Mcomp)
M3.data <- subset(M3,"yearly")

train.data = list()
future.data = list()
for (i in 1:645)
{
  train.data[[i]] = as.numeric(M3.data[[i]]$x)
  future.data[[i]] = as.numeric(M3.data[[i]]$xx)  
}

## Test -- change below to test more series
w.series = 1:20
# w.series = 1:645        # uncomment to test all series

# use 10,000 posterior samples; change n.samples to 20,000 to test that as well if you want
s = system.time({
  rv=blgt.multi.forecast(train.data[w.series], future.data[w.series], n.samples=1e4)
})

s                         # overall timing info
s[[3]] / length(w.series) # per series time

mean(rv$sMAPE)            # performance in terms of mean sMAPE
mean(rv$InCI)/6           # coverage of prediction intervals -- should be close to 95%
}