| predict.far {far} | R Documentation |
Forecasting using FAR(1) or FARX(1) model
predict.far(object, ..., newdata=NULL, label, na.rm=TRUE, positive=FALSE)
object |
A far object result of the far function. |
newdata |
A data matrix (one column for each observation) used to
predict the FAR(1) model from the values in newdata, or NULL
to predict one step forward with the data in object. |
label |
A vector of character giving the dates to associate to the predicted observations. |
na.rm |
Logical. Does the n.a. need to be removed. |
positive |
Logical. Does the result must be forced to positive values. |
... |
Additional arguments. |
This function computes one step forward prediction for a
far model.
Use the newdata option to input the past values,
and the label option value to define the labels for the new
observations. Notices that the output as the same length as
newdata in the case of a FAR model, and the length of
newdata minus one in the case of a FARX model. This is due to
the time shift of the exogeneous variable: Xt+1 and
Yt are used in the computation of
Ypt+1.
In some special context, the user may need to suppress the
na.rm observations with the na.rm option, or force the
prediction to be positive with the positive option (in this
case the result will be maximum of 0 and the predicted value).
A fdata object.
J. Damon
far, pred.persist,
predict.kerfon.
# Simulation of a FARX process
data1 <- simul.farx(m=10,n=400,base=base.simul.far(20,5),
base.exo=base.simul.far(20,5),
d.a=matrix(c(0.5,0),nrow=1,ncol=2),
alpha.conj=matrix(c(0.2,0),nrow=1,ncol=2),
d.rho=diag(c(0.45,0.90,0.34,0.45)),
alpha=diag(c(0.5,0.23,0.018)),
d.rho.exo=diag(c(0.45,0.90,0.34,0.45)),
cst1=0.0)
# Cross validation (joined and separate)
model1.cv <- far.cv(data=data1, y="X", x="Z", kn=8, ncv=10, cvcrit="X",
center=FALSE, na.rm=FALSE, joined=TRUE)
model2.cv <- far.cv(data=data1, y="X", x="Z", kn=c(4,4), ncv=10, cvcrit="X",
center=FALSE, na.rm=FALSE, joined=FALSE)
print(model1.cv)
print(model2.cv)
k1 <- model1.cv$minL2[1]
k2 <- model2.cv$minL2[1:2]
# Modelization of the FARX process (joined and separate)
model1 <- far(data=data1, y="X", x="Z", kn=k1,
center=FALSE, na.rm=FALSE, joined=TRUE)
model2 <- far(data=data1, y="X", x="Z", kn=k2,
center=FALSE, na.rm=FALSE, joined=FALSE)
# Predicting values
pred1 <- predict(model1,newdata=data1)
pred2 <- predict(model2,newdata=data1)
# Persistence
persist1 <- pred.persist(select.fdata(data1,date=1:399),x="X")
# Real values
real1 <- select.fdata(data1,date=2:400)
errors0 <- persist1[[1]]-real1[[1]]
errors1 <- pred1[[1]]-real1[[1]]
errors2 <- pred2[[1]]-real1[[1]]
# Norm of observations
summary(real1)
# Persistence
summary(as.fdata(errors0))
# FARX models
summary(as.fdata(errors1))
summary(as.fdata(errors2))