Returns forecasts using Taylor's (2003) DoubleSeasonal HoltWinters method.
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)
y  Either an 

period1  Period of the shorter seasonal period. Only used if 
period2  Period of the longer seasonal period. Only used if 
h  Number of periods for forecasting. 
alpha  Smoothing parameter for the level. If 
beta  Smoothing parameter for the slope. If 
gamma  Smoothing parameter for the first seasonal period. If

omega  Smoothing parameter for the second seasonal period. If

phi  Autoregressive parameter. If 
lambda  BoxCox transformation parameter. If 
biasadj  Use adjusted backtransformed mean for BoxCox transformations. If transformed data is used to produce forecasts and fitted values, a regular back transformation will result in median forecasts. If biasadj is TRUE, an adjustment will be made to produce mean forecasts and fitted values. 
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. 
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 (onestep forecasts)
Taylor's (2003) doubleseasonal HoltWinters method uses additive trend and
multiplicative seasonality, where there are two seasonal components which
are multiplied together. For example, with a series of halfhourly 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.
Taylor, J.W. (2003) Shortterm electricity demand forecasting using double seasonal exponential smoothing. Journal of the Operational Research Society, 54, 799805.
Hyndman, R.J., Koehler, A.B., Ord, J.K., and Snyder, R.D. (2008) Forecasting with exponential smoothing: the state space approach, SpringerVerlag. http://www.exponentialsmoothing.net.
# 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) # }