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 titled“SVM Kernels for Time Series Analysis.”