dshw

0th

Percentile

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, 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.
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".

    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:

  • modelA list containing information about the fitted model
  • methodThe name of the forecasting method as a character string
  • meanPoint forecasts as a time series
  • xThe original time series (either object itself or the time series used to create the model stored as object).
  • residualsResiduals from the fitted model. That is x minus fitted values.
  • fittedFitted 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. http://www.exponentialsmoothing.net.

See Also

HoltWinters, ets.

Aliases
  • dshw
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 5.3, License: GPL (>= 2)

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