forecast (version 9.0.0)

dshw: Double-Seasonal Holt-Winters Forecasting

Description

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

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
)

Value

An object of class forecast.

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. If lambda = "auto", then a transformation is automatically selected using BoxCox.lambda. The transformation is ignored if NULL. Otherwise, data transformed before model is estimated.

biasadj

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.

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.

Author

Rob J Hyndman

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.

References

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. https://robjhyndman.com/expsmooth/.

See Also

stats::HoltWinters(), ets().

Examples

if (FALSE) {
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, 0.1))
fit <- dshw(x, 20, 5)
plot(fit)
}