A Hybrid System Integrating a Wavelet and TSK Fuzzy Rules for Stock Price Forecasting

The prediction of future time series values based on past and present information is very useful and necessary for various industrial and financial applications. In this study, a novel approach that integrates the wavelet and Takagi-Sugeno-Kang (TSK)-fuzzy-rule-based systems for stock price prediction is developed. A wavelet transform using the Haar wavelet will be applied to decompose the time series in the Haar basis. From the hierarchical scalewise decomposition provided by the wavelet transform, we will next select a number of interesting representations of the time series for further analysis. Then, the TSK fuzzy-rule-based system is employed to predict the stock price based on a set of selected technical indices. To avoid rule explosion, the k-means algorithm is applied to cluster the data and a fuzzy rule is generated in each cluster. Finally, a K nearest neighbor (KNN) is applied as a sliding window to further fine-tune the forecasted result from the TSK model. Simulation results show that the model has successfully forecasted the price variation for stocks with accuracy up to 99.1% in Taiwan Stock Exchange index. Comparative studies with existing prediction models indicate that the proposed model is very promising and can be implemented in a real-time trading system for stock price prediction.

I. Introduction

Mining stock market tendencies is a challenging task due to its high volatility and noisy environment. Many factors influence the performance of a stock market including political events, general economic conditions, and traders’ expectations. Though stocks and futures traders have relied heavily upon various types of intelligent systems to make trading decisions, the success so far is quite limited [4].