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How to insert a certain model in SVM regarding to fixed kernels

6 messages · Amir Safari, Gabor Grothendieck, David Meyer

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David, Please correct me if I am wrong but I think svm partially works
with dyn although I don't remember what the specific limitations were.
Its possible that what works already is enough for Amir. For example,

library(e1071)
library(dyn)
set.seed(1)
y <- ts(rnorm(100))
y.svm <- dyn$svm(y ~ lag(y))
yp <- predict(y.svm)
ts.plot(y, yp, col = 1:2)
On 8/12/05, David Meyer <david.meyer at wu-wien.ac.at> wrote:
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On 8/12/05, Gabor Grothendieck <ggrothendieck at gmail.com> wrote:
The above statement should have been y.svm <- dyn$svm(y ~ lag(y,-1))
since we want to bring the previous value of y forward so that it is being
used to predict y (rather than predicting y by bringing the future value of y
backward).  In R positive values for the lag move the series backward
and negative values move it forward.
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Amir,
Yes, I think.
Gabor Grothendieck's `dyn` package provides support for the use of
general regression functions for time series analysis, and we are
currently struggling to integrate the e1071 interface into that
framework (but nothing is ready so far). Is it that kind of support you
have been looking for?

Cheers,
David

  
    
#
Yes, the fitted values / residuals can be extracted from the trained
model. The 'newdata' argument of predict() is not functional yet for
time series.

Cheers,
David

  
    
2 days later