ses(x, h=10, level=c(80,95), fan=FALSE,
initial=c("optimal","simple"), alpha=NULL, ...)
holt(x, h=10, damped=FALSE, level=c(80,95), fan=FALSE,
initial=c("optimal","simple"), exponential=FALSE,
alpha=NULL, beta=NULL, ...)
hw(x, h=2*frequency(x), seasonal="additive", damped=FALSE,
level=c(80,95), fan=FALSE, initial=c("optimal","simple"),
exponential=FALSE, alpha=NULL, beta=NULL, gamma=NULL, ...)
hw
model. "additive" or "multiplicative"optimal
, the initial values are optimized along with the smoothing parameters using ets
. If simple
, the initial values are set to valNULL
, it will be estimated.NULL
, it will be estimated.NULL
, it will be estimated.forecast.ets
.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 ets
and associated functions.
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
).forecast(ets(...))
.Hyndman, R.J., Athanasopoulos (2014) Forecasting: principles and practice, OTexts: Melbourne, Australia.
ets
, HoltWinters
, rwf
, arima
.fcast <- holt(airmiles)
plot(fcast)
deaths.fcast <- hw(USAccDeaths,h=48)
plot(deaths.fcast)
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