[R] windowing strategies

[Sean Davis]

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, jmoreira at fe.up.pt 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 : ltorgo at liacc.up.pt
>   4150 PORTO   -  PORTUGAL         WWW   :  
> http://www.liacc.up.pt/~ltorgo
>
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