Standard additive fuzzy system for stock price forecasting

  • Authors:
  • Sang Thanh Do;Thi Thanh Nguyen;Dong-Min Woo;Dong-Chul Park

  • Affiliations:
  • Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology, Vietnam;Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology, Vietnam;Department of Information Engineering, Myongji University, Korea;Department of Information Engineering, Myongji University, Korea

  • Venue:
  • ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part II
  • Year:
  • 2010

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Abstract

Stock price forecasting has attracted tremendous attention of researchers over the past several decades. Many techniques thus have been proposed so far to deal with the problem. This paper presents an application of a computational intelligence technique - a fuzzy inference system, namely Standard Additive Model (SAM), for predicting stock price time series data. The modelling and learning power of the SAM have been benefited to build the model that is capable of prediction functionalities. Experimental results have demonstrated that the proposed approach outperforms the traditional Auto Regressive Moving Average (ARMA) model in terms of the forecasting performance.