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)

## Arguments

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. |

## 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)

## 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.

## 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)