In the gregmisc package, there are the "running" functions. I think
there are specific commands for getting subsets of time series, but I
can't remember them off the top of my head.
Sean
On Sep 3, 2004, at 12:59 PM, Luis Torgo wrote:
On Fri, 2004-09-03 at 15:37, [EMAIL PROTECTED] wrote:
Hello to everybody,
Does anyone has implemented a function for evaluating models using
windowing
strategies, such as growing window or sliding window ones?
The aim is to evaluate regression models on a time series data. I do
not use
cross-validation once data sorted in a radom way does not make sense
when
evaluating time series.
I include two functions I've written that I think accomplish what you
want. They return a list with probably too many unnecessary components
for you (they were useful in the context I've used them), so you will
probably want to change that part.
Hope it helps,
Luis Torgo
#===================================================================
# This function allows the execution of sliding window tests using
# any algorithm.
# Example:
# > p.rt <-
sliding.window.testing(exp[1:800,],700,'rpart',learner.pars=list(fk5 ~
.))
# > p.nn <- sliding.window.testing(exp[1:800,],700,'nnet',
# learner.pars=list(fk5 ~
.,size=10,linout=T),
# relearn.step=7)
# Note: This last example only re-learns a new model every 7 cases
#---------------------------------------------------------------------
sliding.window.testing <- function(orig.data, window.size,
learner, learner.pars,
relearn.step=1,
test.pos=window.size+1) {
init.test <- test.pos
n <- nrow(orig.data)
preds <- vector()
while (test.pos <= n) {
cat('*')
learner.pars$data <-
orig.data[(test.pos-window.size):(test.pos-1),]
model <- do.call(learner,learner.pars)
preds <-
c(preds,predict(model,orig.data[test.pos:min(n,test.pos+relearn.step
-1),]))
test.pos <- test.pos+relearn.step
}
cat('\n')
list(train.period=c(test.pos-relearn.step-window.size,test.pos-
relearn.step-1),
model.call=list(func=learner,pars=learner.pars),
model=model,
test.period=c(init.test,n),
preds=preds,
preds.close=NULL,preds.ret=NULL,err.ret=NULL,weigh.preds=NULL)
}
#
======================================================================
# This function allows the execution of growing window tests using any
# algorithm.
# Example:
# > p.rt <-
growing.window.testing(exp[1:800,],700,'rpart',learner.pars=list(fk5 ~
.))
# > p.nn <- growing.window.testing(exp[1:800,],700,'nnet',
# learner.pars=list(fk5 ~
.,size=10,linout=T),
# relearn.step=7)
# Note: This last example only re-learns a new model every 7 cases
#
----------------------------------------------------------------------
growing.window.testing <- function(orig.data,
learner, learner.pars,
relearn.step=1, test.pos) {
init.test <- test.pos
n <- nrow(orig.data)
preds <- vector()
while (test.pos <= n) {
cat('*')
learner.pars$data <- orig.data[1:(test.pos-1),]
model <- do.call(learner,learner.pars)
preds <-
c(preds,predict(model,orig.data[test.pos:min(n,test.pos+relearn.step
-1),]))
test.pos <- test.pos+relearn.step
}
cat('\n')
list(model.call=list(func=learner,pars=learner.pars),
model=model,
test.period=c(init.test,n),
preds=preds)
}
--
Luis Torgo
FEP/LIACC, University of Porto Phone : (+351) 22 607 88 30
Machine Learning Group Fax : (+351) 22 600 36 54
R. Campo Alegre, 823 email : [EMAIL PROTECTED]
4150 PORTO - PORTUGAL WWW :
http://www.liacc.up.pt/~ltorgo
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