external regressors in ahead::dynrmf’s interface for Machine learning forecasting
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options(repos = c(
techtonique = "https://r-packages.techtonique.net",
CRAN = "https://cloud.r-project.org"
))
install.packages(c("ahead", "misc", "fpp2", "glmnet"))
sets <- list(USAccDeaths, AirPassengers, fpp2::a10, fdeaths)
# Default: ridge
par(mfrow=c(2, 2))
for (x in sets)
{
xreg <- ahead::createtrendseason(x)
train_test_x <- misc::splitts(x, split_prob = 0.8)
xreg_training <- window(xreg, start=start(train_test_x$training),
end=end(train_test_x$training))
xreg_testing <- window(xreg, start=start(train_test_x$testing),
end=end(train_test_x$testing))
h <- length(train_test_x$testing)
plot(ahead::dynrmf(y=train_test_x$training,
xreg_fit=xreg_training,
xreg_predict=xreg_testing,
level=99,
h=h))
}
# Default: glmnet::cv.glmnet
par(mfrow=c(2, 2))
for (x in sets)
{
xreg <- ahead::createtrendseason(x)
train_test_x <- misc::splitts(x, split_prob = 0.8)
xreg_training <- window(xreg, start=start(train_test_x$training),
end=end(train_test_x$training))
xreg_testing <- window(xreg, start=start(train_test_x$testing),
end=end(train_test_x$testing))
h <- length(train_test_x$testing)
plot(ahead::dynrmf(y=train_test_x$training,
xreg_fit=xreg_training,
xreg_predict=xreg_testing,
fit_func = glmnet::cv.glmnet,
level=99,
h=h))
}
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