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

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3 Author(s)
Pei-Chann Chang ; Yuan Ze Univ., Taoyuan ; Chin-Yuan Fan ; Shih-Hsin Chen

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.

Published in:

Fuzzy Systems and Knowledge Discovery, 2007. FSKD 2007. Fourth International Conference on  (Volume:3 )

Date of Conference:

24-27 Aug. 2007