# forecast.stl

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##### Forecasting using stl objects

Returns forecasts obtained by either ETS or ARIMA models applied to the seasonally adjusted data from an STL decomposition.

Keywords
ts
##### Usage
## S3 method for class 'stl':
forecast(object, method=c("ets","arima", "naive", "rwdrift"), 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, robust=FALSE, method=c("ets","arima", "naive", "rwdrift"), etsmodel="ZZN",
level=c(80,95), fan=FALSE, lambda=NULL, ...)
##### Arguments
object
An object of class "stl". Usually the result of a call to stl.
x
A univariate numeric time series of class "ts"
method
Method to use for forecasting the seasonally adjusted series.
etsmodel
The ets model specification passed to ets. By default it allows any non-seasonal model. If method!="ets", this argument is ignored.
h
Number of periods for forecasting.
level
Confidence level for prediction intervals.
fan
If TRUE, level is set to seq(50,99,by=1). This is suitable for fan plots.
lambda
Box-Cox transformation parameter. Ignored if NULL. Otherwise, data transformed before model is estimated and back-transformed after forecasts are computed.
s.window
Either the character string "periodic" (default) or the span (in lags) of the loess window for seasonal extraction.
robust
If TRUE, robust fitting will used in the loess procedure within stl.
...
Other arguments passed to ets() or auto.arima().
##### Details

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.

##### Value

• An object of class "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:

• modelA list containing information about the fitted model
• methodThe name of the forecasting method as a character string
• meanPoint forecasts as a time series
• lowerLower limits for prediction intervals
• upperUpper limits for prediction intervals
• levelThe confidence values associated with the prediction intervals
• xThe original time series (either object itself or the time series used to create the model stored as object).
• residualsResiduals from the fitted model. That is (possibly tranformed) x minus fitted values.
• fittedFitted values (one-step forecasts) on transformed scale if lambda is not NULL.

forecast.ets, forecast.Arima.

• forecast.stl
• stlf
• stl
##### Examples
fit <- stl(USAccDeaths,s.window="periodic")
plot(forecast(fit))

plot(stlf(AirPassengers, lambda=BoxCox.lambda(AirPassengers)))
Documentation reproduced from package forecast, version 3.18, License: GPL (>= 2)

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