Forecasting time series
forecast is a generic function for forecasting from time series or time series models.
The function invokes particular methods which depend on the class of the first argument.
forecast(object,...) ## S3 method for class 'ts': forecast(object, h = ifelse(frequency(object) > 1, 2 * frequency(object), 10) , level=c(80,95), fan=FALSE, robust=FALSE, lambda=NULL, find.frequency=FALSE, allow.multiplicative.trend=FALSE, ...)
- a time series or time series model for which forecasts are required
- Number of periods for forecasting
- Confidence level for prediction intervals.
- If TRUE,
levelis set to
seq(50,99,by=1). This is suitable for fan plots.
- If TRUE, the function is robust to missing values and outliers in
object. This argument is only valid when
objectis of class
- Box-Cox transformation parameter.
- If TRUE, the function determines the appropriate period, if the data is of unknown period.
- If TRUE, then ETS models with multiplicative trends are allowed. Otherwise, only additive or no trend ETS models are permitted.
- Additional arguments affecting the forecasts produced.
forecast.tspasses these to
stlfdepending on the frequency of the time
- An object of class "
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 accessors functions
residualsextract various useful features of the value returned by
An object of class
"forecast"is a list usually 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. For models with additive errors, the residuals will be x minus the fitted values. fitted Fitted values (one-step forecasts)