How to insert a certain model in SVM regarding to fixed kernels
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.
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
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:
Amir,
Suppose that we want to regress for example a certain autoregressive model using SVM. We have our data and also some fixed kernels in libSVM behinde e1071 in front. The question: Where can we insert our certain autoregressive model ? During creating data frame ?
Yes, I think.
Or perhaps we can make a relationship between our variables ended to desired autoregressive model ?
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
Thanks a lot for your help. Amir Safari
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Dr. David Meyer Department of Information Systems and Operations Vienna University of Economics and Business Administration Augasse 2-6, A-1090 Wien, Austria, Europe Fax: +43-1-313 36x746 Tel: +43-1-313 36x4393 HP: http://wi.wu-wien.ac.at/~meyer/