# thetaf

##### Theta method forecast

Returns forecasts and prediction intervals for a theta method forecast.

- Keywords
- ts

##### Usage

`thetaf(y, h=ifelse(frequency(y)>1, 2*frequency(y), 10), level=c(80,95), fan=FALSE, x=y)`

##### Arguments

- y
- 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(51,99,by=3). This is suitable for fan plots.
- x
- Deprecated. Included for backwards compatibility.

##### 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).

The series is tested for seasonality using the test outlined in A&N. If deemed seasonal, the series is seasonally adjusted using a classical multiplicative decomposition before applying the theta method. The resulting forecasts are then reseasonalized.

Prediction intervals are computed using the underlying state space model.

More general theta methods are available in the `forecTheta`

package.

##### Value

`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:
is a list containing at least the following elements:##### 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

##### Examples

```
plot(nile.fcast)
```

*Documentation reproduced from package forecast, version 7.2, License: GPL (>= 2)*