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Financial Time Series Prediction in Cooperating with Event Knowledge: A Fuzzy Approach

  • Do-Thanh Sang
  • Dong-Min Woo
  • Dong-Chul Park
  • Thi Nguyen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6438)

Abstract

A number of researchers have used historical numeric time series data to forecast financial markets, i.e. stock prices, and they achieved some results with reasonable accuracies. However, there are various non-numerical factors that influence prices such as company’s performance, government involvement, trends of the market, changes in economic activity and so forth. We attempt to take such factors into account to our recent study. This paper surveys an application of a fuzzy inference system, namely Standard Additive Model, for predicting stock prices in cooperating with event-knowledge and several new training criteria. Experimental results show that the integrated model yields the outcomes which have error smaller than original model’s one.

Keywords

Standard Additive Fuzzy System financial time series prediction event knowledge fuzzy logic 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Do-Thanh Sang
    • 1
  • Dong-Min Woo
    • 1
  • Dong-Chul Park
    • 1
  • Thi Nguyen
    • 2
  1. 1.Department of Electronics EngineeringMyongji UniversityKorea
  2. 2.School of Geography and Environmental ScienceMonash UniversityAustralia

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