Returns forecasts using Taylor's (2003) Double-Seasonal Holt-Winters 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 )
Period of the shorter seasonal period. Only used if
Period of the longer seasonal period. Only used if
Number of periods for forecasting.
Smoothing parameter for the level. If
Smoothing parameter for the slope. If
Smoothing parameter for the first seasonal period. If
Smoothing parameter for the second seasonal period. If
Autoregressive parameter. If
Box-Cox transformation parameter. If
Use adjusted back-transformed mean for Box-Cox 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.
If TRUE, the forecasts are adjusted using an AR(1) model for the errors.
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
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)
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
period1=48 for the daily period and
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
Taylor, J.W. (2003) Short-term electricity demand forecasting using double seasonal exponential smoothing. Journal of the Operational Research 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.
Rob J Hyndman