Dear David, Dear Gabor , Dear All,
Many thanks for your reply and informative emails.
Actually I think it is difficult to define for example a regression model
within a SVM framework theoretically and experimentaly. What we have to do is
that we work on input data to construct model befor
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
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 ~
On 8/12/05, Gabor Grothendieck [EMAIL PROTECTED] wrote:
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)
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.
Dear David,
Dear R Users ,
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 ? Or
Dear R Users ,
Suppose that we want to regress 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 ? Or perhaps we can make
