HXCS and its application to financial time series forecasting
Shumei Liu, Tomoharu Nagao · IEEJ Transactions on Electrical and Electronic Engineering · 2006
Many techniques have been used to predict financial time series data in order to make profitable transaction decisions. Conventional time series are usually identified as a global model. However, in the financial world, time series fluctuates rapidly in time, and so are difficult to be recognized by a single model. Therefore here, we propose a Hierarchical eXtended Classifier System (XCS) model, which is composed of multiple local models. Each local model represents an individual agent. In the lower levels of the hierarchy, agents are trained by the XCS method to learn and forecast. These agents are only appropriate for some of the changing patterns in the time series data, and they fail to describe other changing patterns. For the upper levels of the hierarchy, Reinforcement Learning (RL) is used to determine how to shift among those local models for a changing trend. With the hierarchical learning structure, multiple agents work alternatively and the limitation of a single agent can be overcome. To evaluate the prediction performance, we mainly adopt the prediction accuracy of changing tendency of the next time (up or down), which is measured by the changing direction hit-rate. Experiments have been performed on several well-known stock indexes and stock markets. The results show that the proposed method achieves good performance. © 2006 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.