Returns forecasts and other information for univariate ARIMA models.
# S3 method for fracdiff
forecast(
object,
h = 10,
level = c(80, 95),
fan = FALSE,
simulate = FALSE,
bootstrap = FALSE,
innov = NULL,
npaths = 5000,
lambda = object$lambda,
biasadj = attr(lambda, "biasadj"),
...
)# S3 method for Arima
forecast(
object,
h = if (object$arma[5] > 1) 2 * object$arma[5] else 10,
level = c(80, 95),
fan = FALSE,
xreg = NULL,
simulate = FALSE,
bootstrap = FALSE,
innov = NULL,
npaths = 5000,
lambda = object$lambda,
biasadj = attr(lambda, "biasadj"),
...
)
# S3 method for ar
forecast(
object,
h = 10,
level = c(80, 95),
fan = FALSE,
simulate = FALSE,
bootstrap = FALSE,
innov = NULL,
npaths = 5000,
lambda = NULL,
biasadj = FALSE,
...
)
An object of class forecast.
An object of class Arima, ar or fracdiff. Usually
the result of a call to stats::arima(), auto.arima(), stats::ar(),
arfima() or fracdiff::fracdiff().
Number of periods for forecasting. If xreg is used, h
is ignored and the number of forecast periods is set to the number of rows
of xreg.
Confidence levels for prediction intervals.
If TRUE, level is set to seq(51, 99, by = 3).
This is suitable for fan plots.
If TRUE, prediction intervals are produced by simulation rather
than using analytic formulae. Errors are assumed to be normally distributed.
If TRUE, then prediction intervals are produced by
simulation using resampled errors (rather than normally distributed errors). Ignored if innov is not NULL.
Optional matrix of future innovations to be used in
simulations. Ignored if simulate = FALSE. If provided, this overrides the bootstrap argument. The matrix
should have h rows and npaths columns.
Number of sample paths used in computing simulated prediction intervals.
Box-Cox transformation parameter. If lambda = "auto",
then a transformation is automatically selected using BoxCox.lambda.
The transformation is ignored if NULL. Otherwise,
data transformed before model is estimated.
Use adjusted back-transformed mean for Box-Cox
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.
Other arguments are ignored.
Future values of any regression variables. A numerical vector or matrix of external regressors; it should not be a data frame.
An object of class forecast is a list usually containing at least
the following elements:
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.
Residuals from the fitted model. For models with additive errors, the residuals will be x minus the fitted values.
Fitted values (one-step forecasts)
The function summary can be used to obtain and print a summary of the
results, while the functions plot and autoplot produce plots of the forecasts and
prediction intervals. The generic accessors functions fitted.values and residuals
extract various useful features from the underlying model.
Rob J Hyndman
For Arima or ar objects, the function calls stats::predict.Arima() or
stats::predict.ar and constructs an object of class forecast from the
results. For fracdiff objects, the calculations are all done within
fracdiff::fracdiff() using the equations given by Peiris and Perera (1988).
Peiris, M. & Perera, B. (1988), On prediction with fractionally differenced ARIMA models, Journal of Time Series Analysis, 9(3), 215-220.
stats::predict.Arima(), stats::predict.ar(), auto.arima(),
Arima(), stats::arima(), stats::ar(), arfima().
fit <- Arima(WWWusage, c(3, 1, 0))
plot(forecast(fit))
library(fracdiff)
x <- fracdiff.sim(100, ma = -0.4, d = 0.3)$series
fit <- arfima(x)
plot(forecast(fit, h = 30))
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