tts.var: Estimate Vector AutoregRessive model by 'tts.caret'

In iForecast: Machine Learning Time Series Forecasting

Estimate Vector AutoregRessive model by tts.caret

Description

It estimate VAR model by tts.caret, and generates an object list for multistep forecasts.

Usage

tts.var(
  data,
  p,
  method,
  train.end,
  type,
  trace=TRUE)

Arguments

data	The time series object of the VAR dataset, for example, timeSeries,xts, or zoo. y can be either binary or continuous. Date format must be "
p	The lag order as in VAR(p).
method	The train_model_list of caret. While using this, make sure that the method allows regression. Methods in c("svm","rf","rpart","gamboost","BstLm","bstSm","blackboost") are feasible.
train.end	The end date of training data, must be specificed.The default dates of train.start and test.end are the start and the end of input data; and the test.start is the 1-period next of train.end.
type	The time dummies variables. We have four selection: "none"=no other variables, "trend"=inclusion of time dummy, "season"=inclusion of seasonal dummies, "both"=inclusion of both trend and season. No default.
trace	Whether to print the looping information. The defaut is TRUE.

Details

This function calls tts.caret of package to execute VAR estimation.

Value

output	Output list object generated.
method	The method used.
type	Type of time dummies used, inherited from type of tts.var
data	The complete data structure

Author(s)

Ho Tsung-wu <[e-mail]>, College of Management, National Taiwan Normal University.

Examples

data(macrodata)
y=timeSeries::as.timeSeries(macrodata[,-1])
VLD=window(y,start="2019-01-01",end=end(y))
#OUT1=tts.var(data=y,
#             p=3,
#             method="enet",
#             train.end="2018-12-01",
#             type=c("none","trend","season","both")[1])

#fcst_ml=iForecast.var(OUT1, n.ahead=nrow(VLD))

iForecast documentation built on June 28, 2025, 5:06 p.m.