Forecasting using stl objects
Returns forecasts obtained by either ETS or ARIMA models applied to the seasonally adjusted data from an STL decomposition.
## S3 method for class 'stl': forecast(object, method=c("ets","arima"), h=frequency(object$time.series)*2, level=c(80,95), fan=FALSE, ...) stlf(x, h=frequency(x)*2, s.window=7, method=c("ets","arima"), lambda=NULL, level=c(80,95), fan=FALSE, ...)
- An object of class "
stl". Usually the result of a call to
- A univariate numeric time series of class "
- Either the character string "periodic" (default) or the span (in lags) of the loess window for seasonal extraction.
- Method to use for forecasting the seasonally adjusted series.
- Box-Cox transformation parameter. Ignored if NULL. Otherwise, data transformed before model is estimated.
- Number of periods for forecasting.
- Confidence level for prediction intervals.
- If TRUE, level is set to seq(50,99,by=1). This is suitable for fan plots.
- Other arguments passed to
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
- An object of class "
forecast". The function
summaryis used to obtain and print a summary of the results, while the function
plotproduces a plot of the forecasts and prediction intervals. The generic accessor functions
residualsextract useful features of the value returned by
forecast.stl. An object of class
"forecast"is a list containing at least the following elements:
model A list containing information about the fitted model method The name of the forecasting method as a character string mean Point forecasts as a time series lower Lower limits for prediction intervals upper Upper limits for prediction intervals level The confidence values associated with the prediction intervals x The original time series (either
objectitself or the time series used to create the model stored as
residuals Residuals from the fitted model. That is (possibly tranformed) x minus fitted values. fitted Fitted values (one-step forecasts) on transformed scale if lambda is not NULL.
fit <- stl(USAccDeaths,s.window="periodic") plot(forecast(fit)) plot(stlf(AirPassengers, lambda=BoxCox.lambda(AirPassengers)))