Returns forecasts and other information for univariate ARIMA models.
# S3 method for fracdiff forecast(object, h = 10, level = c(80, 95), fan = FALSE, lambda = object$lambda, biasadj = NULL, ...) # S3 method for Arima forecast(object, h = ifelse(object$arma > 1, 2 * object$arma, 10), level = c(80, 95), fan = FALSE, xreg = NULL, lambda = object$lambda, bootstrap = FALSE, npaths = 5000, biasadj = NULL, ...) # S3 method for ar forecast(object, h = 10, level = c(80, 95), fan = FALSE, lambda = NULL, bootstrap = FALSE, npaths = 5000, biasadj = FALSE, ...)
Number of periods for forecasting. If
Confidence level for prediction intervals.
Box-Cox transformation parameter. Ignored if NULL. Otherwise, forecasts back-transformed via an inverse Box-Cox transformation.
Use adjusted back-transformed mean for Box-Cox transformations. If TRUE, point forecasts and fitted values are mean forecast. Otherwise, these points can be considered the median of the forecast densities. By default, the value is taken from what was used when fitting the model.
Future values of an regression variables (for class
Number of sample paths used in computing simulated prediction
An object of class "
summary is used to obtain and print a summary of the
results, while the function
plot produces a plot of the forecasts and
The generic accessor functions
extract useful features of the value returned by
An object of class "
forecast" is a list containing at least the
A list containing information about the fitted model
The name of the forecasting method as a character string
Point forecasts as a time series
Lower limits for prediction intervals
Upper limits for prediction intervals
The confidence values associated with the prediction intervals
The original time series
object itself or the time series used to create the model
Residuals from the fitted model. That is x minus fitted values.
Fitted values (one-step forecasts)
ar objects, the function calls
constructs an object of class "
forecast" from the results. For
fracdiff objects, the calculations are all done within
forecast.fracdiff using the equations given by Peiris and
Peiris, M. & Perera, B. (1988), On prediction with fractionally differenced ARIMA models, Journal of Time Series Analysis, 9(3), 215-220.
library(fracdiff) x <- fracdiff.sim( 100, ma=-.4, d=.3)$series fit <- arfima(x) plot(forecast(fit,h=30))