forecast.stl
Forecasting using stl objects
Forecasts of STL objects are obtained by applying a nonseasonal forecasting method to the seasonally adjusted data and reseasonalizing using the last year of the seasonal component.
 Keywords
 ts
Usage
stlm(y, s.window=7, robust=FALSE, method=c("ets","arima"), modelfunction=NULL, etsmodel="ZZN", lambda=NULL, xreg=NULL, allow.multiplicative.trend=FALSE, x=y, ...)
stlf(y, h=frequency(x)*2, s.window=7, t.window=NULL, robust=FALSE, lambda=NULL, biasadj=FALSE, x=y, ...)
"forecast"(object, h = 2*object$m,
level = c(80, 95), fan = FALSE, lambda=object$lambda, biasadj=FALSE, newxreg=NULL, allow.multiplicative.trend=FALSE, ...)
"forecast"(object, method=c("ets","arima","naive","rwdrift"), etsmodel="ZZN", forecastfunction=NULL, h=frequency(object$time.series)*2, level=c(80,95), fan=FALSE, lambda=NULL, biasadj=FALSE, xreg=NULL, newxreg=NULL, allow.multiplicative.trend=FALSE, ...)
Arguments
 y
 A univariate numeric time series of class
ts
 object
 An object of class
stl
orstlm
. Usually the result of a call tostl
orstlm
.  method
 Method to use for forecasting the seasonally adjusted series.
 modelfunction
 An alternative way of specifying the function for modelling the seasonally adjusted series. If
modelfunction
is notNULL
, thenmethod
is ignored. Otherwisemethod
is used to specify the time series model to be used.  forecastfunction
 An alternative way of specifying the function for forecasting the seasonally adjusted series. If
forecastfunction
is notNULL
, thenmethod
is ignored. Otherwisemethod
is used to specify the forecasting method to be used.  etsmodel
 The ets model specification passed to
ets
. By default it allows any nonseasonal model. Ifmethod!="ets"
, this argument is ignored.  xreg
 Historical regressors to be used in
auto.arima()
whenmethod=="arima"
.  newxreg
 Future regressors to be used in
forecast.Arima()
.  h
 Number of periods for forecasting.
 level
 Confidence level for prediction intervals.
 fan
 If
TRUE
, level is set to seq(51,99,by=3). This is suitable for fan plots.  lambda
 BoxCox transformation parameter. Ignored if
NULL
. Otherwise, data transformed before decomposition and backtransformed after forecasts are computed.  biasadj
 Use adjusted backtransformed mean for BoxCox transformations. If TRUE, point forecasts and fitted values are mean forecast. Otherwise, these points can be considered the median of the forecast densities.
 s.window
 Either the character string ``periodic'' or the span (in lags) of the loess window for seasonal extraction.
 t.window
 A number to control the smoothness of the trend. See
stl
for details.  robust
 If
TRUE
, robust fitting will used in the loess procedure withinstl
.  allow.multiplicative.trend
 If TRUE, then ETS models with multiplicative trends are allowed. Otherwise, only additive or no trend ETS models are permitted.
 x
 Deprecated. Included for backwards compatibility.
 ...
 Other arguments passed to
forecast.stl
,modelfunction
orforecastfunction
.
Details
stlm
takes a time series y
, applies an STL decomposition, and models the seasonally adjusted data using the model passed as modelfunction
or specified using method
. It returns an object that includes the original STL decomposition and a time series model fitted to the seasonally adjusted data. This object can be passed to the forecast.stlm
for forecasting.forecast.stlm
forecasts the seasonally adjusted data, then reseasonalizes the results by adding back the last year of the estimated seasonal component.stlf
combines stlm
and forecast.stlm
. It takes a ts
argument, applies an STL decomposition, models the seasonally adjusted data, reseasonalizes, and returns the forecasts. However, it allows more general forecasting methods to be specified via forecastfunction
.forecast.stl
is similar to stlf
except that it takes the STL decomposition as the first argument, instead of the time series.
Note that the prediction intervals ignore the uncertainty associated with the seasonal component. They are computed using the prediction intervals from the seasonally adjusted series, which are then reseasonalized using the last year of the seasonal component. The uncertainty in the seasonal component is ignored.
The time series model for the seasonally adjusted data can be specified in stlm
using either method
or modelfunction
. The method
argument provides a shorthand way of specifying modelfunction
for a few special cases. More generally, modelfunction
can be any function with first argument a ts
object, that returns an object that can be passed to forecast
. For example, forecastfunction=ar
uses the ar
function for modelling the seasonally adjusted series.
The forecasting method for the seasonally adjusted data can be specified in stlf
and forecast.stl
using either method
or forecastfunction
. The method
argument provides a shorthand way of specifying forecastfunction
for a few special cases. More generally, forecastfunction
can be any function
with first argument a ts
object, and other h
and level
, which returns an object of class forecast
. For example, forecastfunction=thetaf
uses the thetaf
function for forecasting the seasonally adjusted series.
Value

returns an object of class
stlm
. The other functions return objects of class forecast
.There are many methods for working with forecast
objects including summary
to obtain and print a summary of the
results, while plot
produces a plot of the forecasts and prediction intervals.
The generic accessor functions fitted.values
and residuals
extract useful features.See Also
Examples
tsmod < stlm(USAccDeaths, modelfunction=ar)
plot(forecast(tsmod, h=36))
plot(stlf(AirPassengers, lambda=0))
decomp < stl(USAccDeaths,s.window="periodic")
plot(forecast(decomp))