Theta method forecast
Returns forecasts and prediction intervals for a theta method forecast.
thetaf(x, h=10, level=c(80,95), fan=FALSE)
- a numeric vector or time series
- Number of periods for forecasting
- Confidence levels for prediction intervals.
- If TRUE, level is set to seq(50,99,by=1). This is suitable for fan plots.
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.
- An object of class "
summaryis used to obtain and print a summary of the results, while the function
plotproduces a plot of the forecasts and prediction intervals.
The generic accessor functions
residualsextract useful features of the value returned by
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
objectitself or the time series used to create the model stored as
residuals Residuals from the fitted model. That is x minus fitted values. fitted Fitted values (one-step forecasts)
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.
nile.fcast <- thetaf(Nile) plot(nile.fcast)