demo/LGT&SGT.R

In Rlgt: Bayesian Exponential Smoothing Models with Trend Modifications

library("Rlgt")
#set.seed(12)
options(width=180)

# Use lynx WWWusage for an example
curr_series <- "WWWusage"
data.train <- WWWusage
sizeTestSet <- length(data.train )


mod <- list()
forecasts <- list()

#--------------------------------
#Fit LGT model
mod[["LGT"]] <- rlgt(data.train, 
  control=rlgt.control(MAX_NUM_OF_REPEATS=2, NUM_OF_ITER=2000), 
  verbose=TRUE)
# print the model details
print(mod[["LGT"]])

# print the interval for all vars
posterior_interval(mod[["LGT"]])

forecasts[["LGT"]] <- forecast(mod[["LGT"]], h = sizeTestSet/2, 
                               level=c(80, 95, 98))
plot(forecasts[["LGT"]], main=paste(curr_series,'by LGT'))


# Use AirPassanger data as an example for a seasonal dataset
seasonal_data <- AirPassengers
curr_series <- "AirPassengers"
data.train <- seasonal_data 
sizeTestSet <- frequency(AirPassengers)
#--------------------------------

#Fit SGT model
#y=data.train;  seasonality.type="multiplicative"; error.size.method="std"; level.method="classical"; xreg = NULL; control=rlgt.control(MAX_NUM_OF_REPEATS=2, NUM_OF_ITER=2000);verbose=TRUE; seasonality2=1
mod[["SGT"]] <- rlgt(data.train, 
  control=rlgt.control(MAX_NUM_OF_REPEATS=2, NUM_OF_ITER=3000), 
  verbose=TRUE)
# print the model details
print(mod[["SGT"]])

# print the interval for all vars
posterior_interval(mod[["SGT"]])

forecasts[["SGT"]] <- forecast(mod[["SGT"]], h = sizeTestSet, level=c(80, 95, 98))
plot(forecasts[["SGT"]],main=paste(curr_series,'by SGT'))


#---------------
#Fit SGT model with generalized seasonality
mod[["gSGT"]] <- rlgt(data.train, seasonality.type="generalized",
		control=rlgt.control(MAX_NUM_OF_REPEATS=2, NUM_OF_ITER=3000), 
		verbose=TRUE)

forecasts[["SGT"]] <- forecast(mod[["SGT"]], h = sizeTestSet, level=c(80, 95, 98))
plot(forecasts[["SGT"]],main=paste(curr_series,'by gSGT'))