forecast.robets: Forecasting using ROBETS models

In robets: Forecasting Time Series with Robust Exponential Smoothing

Description

Returns forecasts and other information for univariate ROBETS models.

Usage

## S3 method for class 'robets'
forecast(object, h = ifelse(object$m > 1, 2 * object$m, 10),
  level = c(80, 95), PI = TRUE, lambda = object$lambda, ...)

Arguments

object	An object of class "robets". Usually the result of a call to robets.
h	Number of periods for forecasting
level	Confidence level for prediction intervals.
PI	If TRUE, prediction intervals are calculated.
lambda	Box-Cox transformation parameter. Ignored if NULL. Otherwise, forecasts back-transformed via an inverse Box-Cox transformation.
...	Other arguments.

Details

The code of this function is based on the function forecast.ets of the package forecast of Hyndman and Khandakar (2008).

Value

An object of class "forecast". The function summary is used to obtain and print a summary of the results, while the function plot produces a plot of the forecasts. The generic accessor functions fitted.values and residuals extract useful features of the value returned by forecast.robets. An object of class "forecast" is a list containing at least the following elements:

• model: A list containing information about the fitted model

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

• mean: Point forecasts as a time series

• x: The original time series (either object itself or the time series used to create the model stored as object).

• residuals: Residuals from the fitted model. For models with additive errors, the residuals are x - fitted values. For models with multiplicative errors, the residuals are equal to x /(fitted values) - 1.

• fitted: Fitted values (one-step ahead forecasts)

Author(s)

Ruben Crevits, [e-mail], https://rcrevits.wordpress.com/research

References

Crevits, R., and Croux, C (2016) "Forecasting with Robust Exponential Smoothing with Damped Trend and Seasonal Components".Working paper. https://doi.org/10.13140/RG.2.2.11791.18080

Hyndman, R. J., and Khandakar, Y (2008) "Automatic time series forecasting: The forecasting package for R".Journal of Statistical Software 27(3). https://doi.org/10.18637/jss.v027.i03

See Also

robets

Examples

library(forecast)
model <- robets(nottem)
plot(forecast(model))

Example output

robets documentation built on May 2, 2019, 6:51 a.m.