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.”
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
This is the sample Rapidminer process I used in Video #14. Just download the text file and import into into RM using the import process function. Please note, you will need to create the Excel spreadsheet yourself, as I show you in the video. Just save the Excel to a 2003 format and you’re done.
Expression Engine = Terrible
Textpattern = Terrible
Never again will I be switching from WordPress.
Below is a simple parameter optimization process in Rapidminer using the Iris data set. Download the TXT file and import it into Rapidminer. Of course, you may use whatever data set you want and switch out the learner. Make sure to update the parameter optimization operator parameters. 🙂