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.
# S3 method for stl forecast(object, method = c("ets", "arima", "naive", "rwdrift"), etsmodel = "ZZN", forecastfunction = NULL, h = frequency(object$time.series) * 2, level = c(80, 95), fan = FALSE, lambda = NULL, biasadj = NULL, xreg = NULL, newxreg = NULL, allow.multiplicative.trend = FALSE, ...) stlm(y, s.window = 13, robust = FALSE, method = c("ets", "arima"), modelfunction = NULL, model = NULL, etsmodel = "ZZN", lambda = NULL, biasadj = FALSE, xreg = NULL, allow.multiplicative.trend = FALSE, x = y, ...) # S3 method for stlm forecast(object, h = 2 * object$m, level = c(80, 95), fan = FALSE, lambda = object$lambda, biasadj = NULL, newxreg = NULL, allow.multiplicative.trend = FALSE, ...) stlf(y, h = frequency(x) * 2, s.window = 13, t.window = NULL, robust = FALSE, lambda = NULL, biasadj = FALSE, x = y, ...)
object  An object of class 

method  Method to use for forecasting the seasonally adjusted series. 
etsmodel  The ets model specification passed to

forecastfunction  An alternative way of specifying the function for
forecasting the seasonally adjusted series. If 
h  Number of periods for forecasting. 
level  Confidence level for prediction intervals. 
fan  If 
lambda  BoxCox transformation parameter. If 
biasadj  Use adjusted backtransformed mean for BoxCox transformations. If transformed data is used to produce forecasts and fitted values, a regular back transformation will result in median forecasts. If biasadj is TRUE, an adjustment will be made to produce mean forecasts and fitted values. 
xreg  Historical regressors to be used in

newxreg  Future regressors to be used in

allow.multiplicative.trend  If TRUE, then ETS models with multiplicative trends are allowed. Otherwise, only additive or no trend ETS models are permitted. 
...  Other arguments passed to 
y  A univariate numeric time series of class 
s.window  Either the character string ``periodic'' or the span (in lags) of the loess window for seasonal extraction. 
robust  If 
modelfunction  An alternative way of specifying the function for
modelling the seasonally adjusted series. If 
model  Output from a previous call to 
x  Deprecated. Included for backwards compatibility. 
t.window  A number to control the smoothness of the trend. See

stlm
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.
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.
plot(stlf(AirPassengers, lambda=0))