## S3 method for class 'stl':
forecast(object, method=c("ets","arima"), etsmodel="ZZN",
h=frequency(object$time.series)*2, level=c(80,95), fan=FALSE, lambda=NULL, ...)
stlf(x, h=frequency(x)*2, s.window=7, method=c("ets","arima"), etsmodel="ZZN",
level=c(80,95), fan=FALSE, lambda=NULL, ...)
stl
". Usually the result of a call to stl
.ts
"ets
. By default it allows any non-seasonal model. If method=="arima"
, this argument is ignored.ets()
or auto.arima()
.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 forecast.stl
.
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.stl
seasonally adjusts the data from an STL decomposition, then uses either ETS or ARIMA models to forecast the result. The seasonal component from the last year of data is added back in to the forecasts. Note that the prediction intervals ignore the uncertainty associated with the seasonal component.
stlf
takes a ts
argument and applies a stl decomposition before calling forecast.stl
.forecast.ets
, forecast.Arima
.fit <- stl(USAccDeaths,s.window="periodic")
plot(forecast(fit))
plot(stlf(AirPassengers, lambda=BoxCox.lambda(AirPassengers)))
Run the code above in your browser using DataLab