Cluster-based genetic segmentation of time series with DWT
Pattern Recognition Letters
An inter-market arbitrage trading system based on extended classifier systems
Expert Systems with Applications: An International Journal
A hybrid SARIMA wavelet transform method for sales forecasting
Decision Support Systems
A dynamic threshold decision system for stock trading signal detection
Applied Soft Computing
A GMDH-based fuzzy modeling approach for constructing TS model
Fuzzy Sets and Systems
Artificial Intelligence in Medicine
International Journal of Data Analysis Techniques and Strategies
A scale-space-based system for time series prediction
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
A hybrid FLANN and adaptive differential evolution model for forecasting of stock market indices
International Journal of Knowledge-based and Intelligent Engineering Systems
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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.