Financial Time Series Data Forecasting by Wavelet and TSK Fuzzy Rule Based System

Authors:
Pei-Chann Chang;Chin-Yuan Fan;Shih-Hsin Chen
Affiliations:
Yuan Ze University, Taoyuan 32026, Taiwan;Yuan Ze University, Taoyuan, Taiwan;Yuan Ze University, Taoyuan, Taiwan
Venue:
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 03
Year:
2007

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Abstract

In this study, a novel approach by integrating the wavelet and Takagi-Sugeno-Kang (TSK) fuzzy rule based systems (FRBS) for financial time series data prediction is developed. The wavelet method is applied to eliminate the noises caused by random fluctuations. The data output from the wavelet is then input to the TSK fuzzy rule system for prediction of the future value of a time series data. Through the intensive experimental tests, the model has successfully forecasted the price variation for stocks with accuracy close to 97.6% in TSE index.