rwf: Random Walk Forecast

Description

Returns forecasts and prediction intervals for a random walk with drift model applied to x.

Usage

rwf(x, h=10, drift=FALSE, level=c(80,95), fan=FALSE, lambda=NULL)

Arguments

a numeric vector or time series

Number of periods for forecasting

drift

Logical flag. If TRUE, fits a random walk with drift model.

level

Confidence levels for prediction intervals.

fan

If TRUE, level is set to seq(50,99,by=1). This is suitable for fan plots.

lambda

Box-Cox transformation parameter. Ignored if NULL. Otherwise, forecasts back-transformed via an inverse Box-Cox transformation.

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 and prediction intervals.
The generic accessor functions fitted.values and residuals extract useful features of the value returned by rwf.
An object of class "forecast" is a list containing at least the following elements:
modelA list containing information about the fitted model
methodThe name of the forecasting method as a character string
meanPoint forecasts as a time series
lowerLower limits for prediction intervals
upperUpper limits for prediction intervals
levelThe confidence values associated with the prediction intervals
xThe original time series (either object itself or the time series used to create the model stored as object).
residualsResiduals from the fitted model. That is x minus fitted values.
fittedFitted values (one-step forecasts)

Details

The random walk with drift model is $$Y_t=c + Y_{t-1} + Z_t$$ where $Z_t$ is a normal iid error. Forecasts are given by $$Y_n(h)=ch+Y_n$$. If there is no drift, the drift parameter c=0. Forecast standard errors allow for uncertainty in estimating the drift parameter.

Examples

Run this code

gold.fcast <- rwf(gold[1:60],h=50)
plot(gold.fcast)