Using SVM Kernels for Time Series Analysis

I wanted to share two research papers that are invaluable to anyone trying to use Support Vector Machines (SVM) for modeling the stock market time series.  SVM Kernels for Time Series are incredibly powerful if you know which one to choose.

Below are two papers, one written by an author well known to the Rapid-I team, and another by Korean researcher.  I’ve used both of these papers as blueprints for some of my past stock market analysis processes.

SVM Kernels for Time Series

The first one is by Kyoung-jae Kim and titled Financial time series forecasting using support vector machines.

The second is by Stefan Ruping (forgive the missing umlaut) and titledSVM Kernels for Time Series Analysis.”

Rapidminer Sample Process – Multiply Data

I often use the Multiply operator to make copies of my data set and feed it into different learners. I do this because sometimes I don’t know if a Neural Net operator, or a SVM operator, give me better performance.  Once I know which operator performs my task better, I then use the parameter optimization process to see if I can squeeze more accuracy out it.

The sample process below uses the Iris data set, just switch it out with your data set and enjoy.

Multiply Sample Process.txt