R: Double-Seasonal Holt-Winters Forecasting

dshw {forecast}

R Documentation

Double-Seasonal Holt-Winters Forecasting

Description

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

Usage

dshw(y, period1, period2, h=2*max(period1,period2), 
    alpha=NULL, beta=NULL, gamma=NULL, omega=NULL, phi=NULL, 
    lambda=NULL, armethod=TRUE)

Arguments

`y`	a numeric vector or time series
`period1`	Period of the shorter seasonal period.
`period2`	Period of the longer seasonal period.
`h`	Number of periods for forecasting
`alpha`	Smoothing parameter for the level.
`beta`	Smoothing parameter for the slope.
`gamma`	Smoothing parameter for the first seasonal period.
`omega`	Smoothing parameter for the second seasonal period.
`phi`	Autoregressive parameter.
`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.

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 and prediction intervals.

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:

`model`	A list containing information about the fitted model
`method`	The name of the forecasting method as a character string
`mean`	Point forecasts as a time series
`lower`	Lower limits for prediction intervals
`upper`	Upper limits for prediction intervals
`level`	The confidence values associated with the prediction intervals
`x`	The original time series (either `object` itself or the time series used to create the model stored as `object`).
`residuals`	Residuals from the fitted model. That is x minus fitted values.
`fitted`	Fitted values (one-step forecasts)

Author(s)

Rob J Hyndman

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.

Examples

## Not run: 
fcast <- dshw(taylor)
plot(fcast)

## End(Not run)

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)

[Package forecast version 3.24 Index]