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 $x$?

  1. Time series? For i.e. in one step ahead prediction $1,2,3...Z$ for $Z$ days?
  2. (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 training_instance_matrix?

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.


1 Answer 1


A common approach is to construct some kind of ARMA model. The easiest way to do so is by windowizing the time series with a certain window length N: stock prices at time $k-N$ to $k-1$ are used to predict the stock price at time $k$. You can, ofcourse, include additional parameters for prediction.

As an example, suppose we have the following univariate time series $s$:

1 2 3 4 5 6 7 8 9 $=s[1]..s[9]$

Windowizing using $N=3$ yields: $$\begin{align} \Big[s[k-3],\ s[k-2],\ s[k-1]\Big] &\rightarrow s[k] \\ \begin{bmatrix} 1 & 2 & 3 \\ 2 & 3 & 4 \\ 3 & 4 & 5 \\ \vdots & \vdots & \vdots \\ 6 & 7 & 8 \end{bmatrix} &\rightarrow \begin{bmatrix} 4 \\ 5 \\ 6 \\ \vdots \\ 9 \end{bmatrix} \end{align}$$

In this example, for each discrete moment $k$, we obtain 3-dimensional $\mathbf{x}$ vectors to predict $y=s[k]$. The window length $N$ becomes a tuning parameter which must be optimized, for example using cross-validation.

  • $\begingroup$ Does that mean k step ahead prediction?? What if we want to use it for multivariate data? Will we also sift the other features k step? $\endgroup$ Commented Jul 23, 2013 at 19:13
  • $\begingroup$ This is one-step ahead prediction, using measurements of the past k steps. If you have several time-dependent features, then yes, windowizing all of them is the best approach. $\endgroup$ Commented Jul 23, 2013 at 19:22
  • $\begingroup$ Actually i want to solve how to get the solutions in some papers like academicjournals.org/ijps/PDF/pdf2012/9Oct/Chen%20et%20al.pdf It is written that one step ahead prediction is used. It has a data which is univariate. And separates the data say %30 for test. What is x and what is y in here for SVR? $\endgroup$ Commented Jul 23, 2013 at 19:39
  • $\begingroup$ Like I explained in my post, y is always the predicted value, e.g. the next time step for one step ahead. x is a vector of the previous time steps with a length of your choosing. $\endgroup$ Commented Jul 23, 2013 at 19:48
  • $\begingroup$ Is it possible to get the length only 1? So the as in your example x will be [1...8] and y will be 2[...9]. Bec. it isn't written the number of length in papers. $\endgroup$ Commented Jul 23, 2013 at 19:52

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