forecast.ARml: Forecasting using ARml model

In Akai01/caretForecast: Conformal Time Series Forecasting Using State of Art Machine Learning Algorithms

Forecasting using ARml model

Description

Forecasting using ARml model

Usage

## S3 method for class 'ARml'
forecast(object, h = frequency(object$y), xreg = NULL, level = c(80, 95), ...)

Arguments

object	An object of class "ARml", the result of a call to ARml.
h	forecast horizon
xreg	Optionally, a numerical vector or matrix of future external regressors
level	Confidence level for prediction intervals.
...	Ignored

Value

A list class of forecast containing the following elemets

• x : The input time series

• method : The name of the forecasting method as a character string

• mean : Point forecasts as a time series

• lower : Lower limits for prediction intervals

• upper : Upper limits for prediction intervals

• level : The confidence values associated with the prediction intervals

• model : A list containing information about the fitted model

• newxreg : A matrix containing regressors

Author(s)

Resul Akay

Examples

library(caretForecast)

train_data <- window(AirPassengers, end = c(1959, 12))

test <- window(AirPassengers, start = c(1960, 1))

ARml(train_data, caret_method = "lm", max_lag = 12) -> fit

forecast(fit, h = length(test), level = c(80,95)) -> fc

autoplot(fc)+ autolayer(test)

accuracy(fc, test)

Akai01/caretForecast documentation built on Oct. 25, 2022, 4:18 a.m.