Returns forecasts and prediction intervals for a theta method forecast.

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

y

a numeric vector or time series of class `ts`

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.

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:

A list containing information about the fitted model

The name of the forecasting method as a character string

Point forecasts as a time series

Lower limits for prediction intervals

Upper limits for prediction intervals

The confidence values associated with the prediction intervals

The original time series
(either `object`

itself or the time series used to create the model
stored as `object`

).

Residuals from the fitted model. That is x minus fitted values.

Fitted values (one-step forecasts)

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.

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.

```
# NOT RUN {
nile.fcast <- thetaf(Nile)
plot(nile.fcast)
# }
```

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