# dshw

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

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

- Keywords
- ts

##### Usage

```
dshw(y, period1 = NULL, period2 = NULL, 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. By default, the value is taken from what was used when fitting the model.

- 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

An object of class "`forecast`

" which is a list that includes 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

The original time series.

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

Fitted values (one-step forecasts)

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.

##### 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 {
# }
# 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)
# }
# NOT RUN {
# }
```

*Documentation reproduced from package forecast, version 8.1, License: GPL (>= 3)*