predict.arfima {arfima}  R Documentation 
Performs prediction of a fitted arfima
object. Includes prediction
for each mode and exact and limiting prediction error standard deviations.
NOTE: the standard errors in beta are currently not taken into
account in the prediction intervals shown. This will be updated as soon
as possible.
## S3 method for class 'arfima' predict(object, n.ahead = 1, prop.use = "default", newxreg = NULL, predint = 0.95, exact = c("default", T, F), setmuhat0 = FALSE, cpus = 1, trend = NULL, n.use = NULL, xreg = NULL, ...)
object 
A fitted 
n.ahead 
The number of steps ahead to predict 
prop.use 
The proportion (between 0 and 1) or percentage (between
>1 and 100) of data points to use for prediction. Defaults to the string
"default", which sets the number of data points 
newxreg 
If a regression fit, the new regressors 
predint 
The percentile to use for prediction intervals assuming normal deviations. 
exact 
Controls whether exact (based on the theoretical autocovariance
matrix) prediction variances are calculated (which is recommended), as well
as whether the exact prediction formula is used when the process is
differenced (which can take a fair amount of time if the length of the series
used to predict is large). Defaults to the string "default", which is

setmuhat0 
Experimental. Sets muhat equal to zero 
cpus 
The number of CPUs to use for prediction. Currently not implemented 
trend 
An optional vector the length of 
n.use 
Directly set the number mentioned in 
xreg 
Alias for newxreg 
... 
Optional arguments. Currently not used 
A list of lists, ceiling(prop.use * n)one for each mode with relavent details about the prediction
JQ (Justin) Veenstra
Veenstra, J.Q. Persistence and Antipersistence: Theory and Software (PhD Thesis)
arfima
, plot.predarfima
,
print.predarfima
set.seed(82365) sim < arfima.sim(1000, model = list(dfrac = 0.4, theta=0.9, dint = 1)) fit < arfima(sim, order = c(0, 1, 1), back=TRUE) fit pred < predict(fit, n.ahead = 5) pred plot(pred, numback=50) #Predictions aren't really different due to the #series. Let's see what happens when we regress! set.seed(23524) #Forecast 5 ahead as before #Note that we need to integrate the regressors, since time series regression #usually assumes that regressors are of the same order as the series. n.fore < 5 X < matrix(rnorm(3000+3*n.fore), ncol = 3) X < apply(X, 2, cumsum) Xnew < X[1001:1005,] X < X[1:1000,] beta < matrix(c(2, .4, 6), ncol = 1) simX < sim + as.vector(X%*%beta) fitX < arfima(simX, order = c(0, 1, 1), xreg = X, back=TRUE) fitX #Let's compare predictions. predX < predict(fitX, n.ahead = n.fore, xreg = Xnew) predX plot(predX, numback = 50) #With the mode we know is really there, it looks better. fitX < removeMode(fitX, 2) predXnew < predict(fitX, n.ahead = n.fore, xreg = Xnew) predXnew plot(predXnew, numback=50) #