# dshw

##### Double-Seasonal Holt-Winters Forecasting

Returns forecasts using Taylor's (2003) Double-Seasonal Holt-Winters method.

- Keywords
- ts

##### Usage

```
dshw(y, period1, period2, h=2*max(period1,period2),
alpha=NULL, beta=NULL, gamma=NULL, omega=NULL, phi=NULL,
lambda=NULL, armethod=TRUE)
```

##### Arguments

- y
- Either an
`msts`

object with two seasonal periods or a numeric vector. - period1
- Period of the shorter seasonal period. Only used if
`y`

is not an`msts`

object. - period2
- Period of the longer seasonal period. Only used if
`y`

is not an`msts`

object. - h
- Number of periods for forecasting.
- alpha
- Smoothing parameter for the level. If
`NULL`

, the parameter is estimated using least squares. - beta
- Smoothing parameter for the slope. If
`NULL`

, the parameter is estimated using least squares. - gamma
- Smoothing parameter for the first seasonal period. If
`NULL`

, the parameter is estimated using least squares. - omega
- Smoothing parameter for the second seasonal period. If
`NULL`

, the parameter is estimated using least squares. - phi
- Autoregressive parameter. If
`NULL`

, the parameter is estimated using least squares. - lambda
- Box-Cox transformation parameter. Ignored if
`NULL`

. Otherwise, data transformed before model is estimated. - armethod
- If TRUE, the forecasts are adjusted using an AR(1) model for the errors.

##### Details

Taylor's (2003) double-seasonal Holt-Winters method uses additive trend and multiplicative seasonality, where there are two seasonal components which are multiplied together. For example, with a series of half-hourly data, one would set `period1=48`

for the daily period and `period2=336`

for the weekly period. The smoothing parameter notation used here is different from that in Taylor (2003); instead it matches that used in Hyndman et al (2008) and that used for the `ets`

function.

##### 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.The generic accessor functions

`fitted.values`

and`residuals`

extract useful features of the value returned by`meanf`

.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 x The original time series (either `object`

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

).residuals Residuals from the fitted model. That is x minus fitted values. fitted Fitted values (one-step forecasts)

##### References

Taylor, J.W. (2003) Short-term electricity demand forecasting using double seasonal exponential smoothing. *Journal of the Operational Reseach Society*, **54**, 799-805.

Hyndman, R.J., Koehler, A.B., Ord, J.K., and Snyder, R.D. (2008)
*Forecasting with exponential smoothing: the state space approach*,
Springer-Verlag.

##### See Also

##### Examples

```
fcast <- dshw(taylor)
plot(fcast)
t <- seq(0,5,by=1/20)
x <- exp(sin(2*pi*t) + cos(2*pi*t*4) + rnorm(length(t),0,.1))
fit <- dshw(x,20,5)
plot(fit)
```

*Documentation reproduced from package forecast, version 4.04, License: GPL (>= 2)*