forecast (version 2.13)

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)

Arguments

x

a numeric vector or time series

h

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.

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:
• 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
• lower
  Lower limits for prediction intervals
• upper
  Upper limits for prediction intervals
• level
  The confidence values associated with the prediction intervals
• 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. That is x minus fitted values.
• fitted
  Fitted 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.

See Also

arima, meanf

Examples

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