A Hybrid System Integrating a Wavelet and TSK Fuzzy Rules for Stock Price Forecasting
- 26 September 2008
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews)
- Vol. 38 (6), 802-815
- https://doi.org/10.1109/tsmcc.2008.2001694
Abstract
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.Keywords
This publication has 53 references indexed in Scilit:
- Evolutionary Fuzzy Neural Networks for Hybrid Financial PredictionIEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), 2005
- AN INVESTIGATION OF THE HYBRID FORECASTING MODELS FOR STOCK PRICE VARIATION IN TAIWANJournal of the Chinese Institute of Industrial Engineers, 2004
- Application of Haar-wavelet-based multiresolution time-domain schemes to electromagnetic scattering problemsIEEE Transactions on Antennas and Propagation, 2002
- Forecasting Market Trends with Neural NetworksInformation Systems Management, 1999
- A neural network based fuzzy set model for organizational decision makingIEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), 1998
- Combining Neural Network Forecasts on Wavelet-transformed Time SeriesConnection Science, 1997
- Using percentage accuracy to measure neural network predictions in Stock Market movementsNeurocomputing, 1996
- Adapting to Unknown Smoothness via Wavelet ShrinkageJournal of the American Statistical Association, 1995
- The role of fuzzy logic in modeling, identification and controlModeling, Identification and Control: A Norwegian Research Bulletin, 1994
- Optimization by Simulated AnnealingScience, 1983