Forecasting using ARIMA or ARFIMA models
Returns forecasts and other information for univariate ARIMA models.
## S3 method for class 'Arima': forecast(object, h=ifelse(object$arma>1,2*object$arma,10), level=c(80,95), fan=FALSE, xreg=NULL, lambda=object$lambda, bootstrap=FALSE, npaths=5000, ...) ## S3 method for class 'ar': forecast(object, h=10, level=c(80,95), fan=FALSE, lambda=NULL, bootstrap=FALSE, npaths=5000, ...) ## S3 method for class 'fracdiff': forecast(object, h=10, level=c(80,95), fan=FALSE, lambda=object$lambda, ...)
- An object of class "
ar" or "
fracdiff". Usually the result of a call to
- Number of periods for forecasting. If
his ignored and the number of forecast periods is set to the number of rows of
- Confidence level for prediction intervals.
TRUE, level is set to
seq(50,99,by=1). This is suitable for fan plots.
- Future values of an regression variables (for class
- Box-Cox transformation parameter. Ignored if NULL. Otherwise, forecasts back-transformed via an inverse Box-Cox transformation.
TRUE, then prediction intervals computed using simulation with resampled errors.
- Number of sample paths used in computing simulated prediction intervals when
- Other arguments.
ar objects, the function calls
constructs an object of class "
forecast" from the results. For
fracdiff objects, the calculations are all done
forecast.fracdiff using the equations given by Peiris and Perera (1988).
- 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 accessor functions
residualsextract useful features of the value returned by
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 x minus fitted values. fitted Fitted values (one-step forecasts)
Peiris, M. & Perera, B. (1988), On prediction with fractionally differenced ARIMA models, Journal of Time Series Analysis, 9(3), 215-220.
fit <- Arima(WWWusage,c(3,1,0)) plot(forecast(fit)) library(fracdiff) x <- fracdiff.sim( 100, ma=-.4, d=.3)$series fit <- arfima(x) plot(forecast(fit,h=30))