# thetaf

0th

Percentile

##### Theta method forecast

Returns forecasts and prediction intervals for a theta method forecast.

Keywords
ts
##### 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.
##### 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.

##### 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)

##### 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.

arima, meanf, rwf, ses
nile.fcast <- thetaf(Nile)
plot(nile.fcast)