forecast (version 3.20)

thetaf: Theta method forecast

Description

Returns forecasts and prediction intervals for a theta method forecast.

Usage

thetaf(x, h=10, level=c(80,95), fan=FALSE)

Arguments

x

a numeric vector or time series

h

Number of periods for forecasting

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 theta method of Assimakopoulos and Nikolopoulos (2000) is equivalent to simple exponential smoothing with drift. This is demonstrated in Hyndman and Billah (2003). Prediction intervals are computed using the underlying state space model.

References

Assimakopoulos, V. and Nikolopoulos, K. (2000). The theta model: a decomposition approach to forecasting. International Journal of Forecasting 16, 521-530.

Hyndman, R.J., and Billah, B. (2003) Unmasking the Theta method. International J. Forecasting, 19, 287-290.

See Also

arima, meanf, rwf, ses

Examples

nile.fcast <- thetaf(Nile)
plot(nile.fcast)