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)
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 meanf
.
An object of class "forecast"
is a list containing at least the following elements:
object
itself or the time series used to create the model stored as object
).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.
HoltWinters
, ets
.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)
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