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

In Rlgt: Bayesian Exponential Smoothing Models with Trend Modifications

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
)

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.

Value

returns a forecast object compatible with the forecast package in R

Examples

## Not run: demo(exampleScript)
## Not run: 
## 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%

## End(Not run)

Rlgt documentation built on Sept. 11, 2024, 7:49 p.m.