setartree (version 0.2.1)

forecast.setartree: Forecast method for SETAR-Tree fits

Description

Obtains forecasts for a given set of time series or a dataframe/matrix of new instances from a fitted SETAR-Tree model.

Usage

# S3 method for setartree
forecast(object, newdata, h = 5, level = c(80, 95), ...)

Value

If newdata is a list of time series, then an object of class mforecast is returned. The plot or autoplot functions in the R forecast package can then be used to produce a plot of any time series in the returned object which contains the following properties.

method

A vector containing the name of the forecasting method ("SETAR-Tree").

forecast

A list of objects of class forecast. Each list object is corresponding with a time series and its forecasts. Each list object contains 7 properties: method (the name of the forecasting method, SETAR-Tree, as a character string), x (the original time series), mean (point forecasts as a time series), series (the name of the series as a character string), upper (upper bound of confidence intervals), lower (lower bound of confidence intervals) and level (confidence level of prediction intervals).

If newdata is a dataframe/matrix, then a list containing the predictions, prediction intervals (upper and lower bounds), the size and standard deviations of the residuals of the models used to get each prediction is returned.

Arguments

object

An object of class setartree which is a trained SETAR-Tree model.

newdata

A list of time series which needs forecasts or a dataframe/matrix of new instances which need predictions.

h

The required number of forecasts (forecast horizon). This parameter is only required when newdata is a list of time series. Default value is 5.

level

Confidence level for prediction intervals. Default value is c(80, 95).

...

Other arguments.

Examples

# \donttest{
# Obtaining forecasts for a list of time series
tree1 <- setartree(chaotic_logistic_series)
forecast(tree1, chaotic_logistic_series)

# Obtaining forecasts for a set of test instances
tree2 <- setartree(data = web_traffic_train[,-1],
                   label = web_traffic_train[,1],
                   stopping_criteria = "both",
                   categorical_covariates = "Project")
forecast(tree2, web_traffic_test)
# }