Has anyone attempted prediction using support vector regression? I'm using LIBSVM, but I'm not sure how to use SVR in either univariate and multivariate time series.
Say we have stock prices for $N$ days. For training inputs, $y$ are the stock prices for $N$ days, but what will we use for $x$?
- Time series? For i.e. in one step ahead prediction $1,2,3...Z$ for $Z$ days?
- (for one step ahead) sifting one day of $y$ values?
To explain more:
matlab> model = svmtrain(training_label_vector, training_instance_matrix [, 'libsvm_options']);
For univariate: I use the stock prices for $N$ days in
training_label_vector as a column vector and want to predict say next 30 days. I wonder which data I have to use in
For multivariate: say I have 22 more features (prices of other goodies), I use other features as column vectors in
training_instance_matrix. But I'm not sure if I'm using the correct approach.