Weighted Kernel Regression for Predicting Changing Dependencies

Abstract. Consider the online regression problem where the dependence of the outcome yt on the signal xt changes with time. Standard regression techniques, like Ridge Regression, do not perform well in tasks of this type. We propose two methods to handle this problem: WeCKAAR, a simple modification of an existing regression technique, and KAARCh, an application of the Aggregating Algorithm. Empirical results on artificial data show that in this setting, KAARCh is superior to WeCKAAR and standard regression techniques. On options implied volatility data, the performance of both KAARCh and WeCKAAR is comparable to that of the proprietary technique currently being used at the Russian Trading System Stock Exchange (RTSSE).

• Type: Conference
• Year: 2007
• Where: ECML
• Authors: Steven Busuttil, Yuri Kalnishkan
• Keywords: ECML 2007, Machine Learning, Online Regression Problem, Regression Techniques, Standard Regression Techniques