predict.TARMA: Forecast from fitted TARMA models.

In tseriesTARMA: Analysis of Nonlinear Time Series Through Threshold Autoregressive Moving Average Models (TARMA) Models

Forecast from fitted TARMA models.

Description

Forecasting with TARMA models

Usage

## S3 method for class 'TARMA'
predict(
  object,
  x,
  n.ahead = 0,
  n.sim = 1000,
  quant = c(0.05, 0.95),
  pred.matrix = FALSE,
  ...
)

Arguments

object	A TARMA fit upon x.
x	The fitted time series.
n.ahead	The number of steps ahead for which prediction is required.
n.sim	The number of Monte Carlo replications used to simulate the prediction density.
quant	Vector of quantiles (in the interval [0, 1]) to be computed upon the prediction density.
pred.matrix	Logical. if TRUE prints also the whole simulated prediction density for each prediction horizon from 1 to n.ahead.
...	Additional arguments.

Details

If n.ahead = 0 it gives the fitted values from the model. If the fit is from TARMA.fit2 and includes covariates, these are ignored.

Value

A list with components pred.matrix, pred, and pred.interval. The latter two are ts objects that contain the prediction and the quantiles of the prediction density, respectively. If pred.matrix = TRUE then the prediction density from which the quantiles are computed is also returned.

Author(s)

Simone Giannerini, [e-mail]

Greta Goracci, [e-mail]

References

• \insertRef

  Gia21tseriesTARMA

See Also

TARMA.fit and TARMA.fit2 for TARMA modelling. plot.tsfit for plotting TARMA fits and forecasts.

Examples

## a TARMA(1,1,1,1) model
set.seed(13)
x1   <- TARMA.sim(n=200, phi1=c(0.5,-0.5), phi2=c(0.0,0.5), theta1=-0.5, theta2=0.7, d=1, thd=0.2)
fit1 <- TARMA.fit(x1, method='L-BFGS-B',tar1.lags = 1, tar2.lags = 1, tma1.lags = 1, 
        tma2.lags = 1, d=1, threshold=0.2)
xp1  <- predict(fit1,x1,n.ahead=2)
xp1

tseriesTARMA documentation built on Oct. 8, 2024, 5:11 p.m.