# 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, biasadj=FALSE, armethod=TRUE, model = NULL)`

##### 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. - biasadj
- Use adjusted back-transformed mean for Box-Cox transformations. If TRUE, point forecasts and fitted values are mean forecast. Otherwise, these points can be considered the median of the forecast densities.
- armethod
- If TRUE, the forecasts are adjusted using an AR(1) model for the errors.
- model
- If it's specified, an existing model is applied to a new data set.

##### 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

`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 `dshw`

.An object of class `"forecast"`

is a list containing at least the following elements:
is a list containing at least the following elements:##### 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. http://www.exponentialsmoothing.net.

##### See Also

##### Examples

```
## Not run:
# 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)
# ## End(Not run)
```

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