dshw(y, period1, period2, h=2*max(period1,period2), alpha=NULL, beta=NULL, gamma=NULL, omega=NULL, phi=NULL, lambda=NULL, biasadj=FALSE, armethod=TRUE, model = NULL)
msts
object with two seasonal periods or a numeric vector.y
is not an msts
object.y
is not an msts
object.NULL
, the parameter is estimated using least squares.NULL
, the parameter is estimated using least squares.NULL
, the parameter is estimated using least squares.NULL
, the parameter is estimated using least squares.NULL
, the parameter is estimated using least squares.NULL
. Otherwise, data transformed before model is estimated.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 dshw
.An object of class "forecast"
is a list containing at least the following elements:
is a list containing at least the following elements: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.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.
HoltWinters
, ets
.## 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)
# ## End(Not run)
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