Personalised Modelling for Multiple Time-Series Data Prediction: A Preliminary Investigation in Asia Pacific Stock Market Indexes Movement

  • Harya Widiputra
  • Russel Pears
  • Nikola Kasabov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5506)

Abstract

The behaviour of multiple stock markets can be described within the framework of complex dynamic systems (CDS). Using a global model with the Kalman Filter we are able to extract the dynamic interaction network (DIN) of these markets. The model was shown to successfully capture interactions between stock markets in the long term. In this study we investigate the effectiveness of two different personalised modelling approaches to multiple stock market prediction. Preliminary results from this study show that the personalised modelling approach when applied to the rate of change of the stock market index is better able to capture recurring trends that tend to occur with stock market data.

Keywords

Stock Market Stock Index Stock Market Index Personalise Modelling Index Future Market 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Harya Widiputra
    • 1
  • Russel Pears
    • 1
  • Nikola Kasabov
    • 1
  1. 1.Knowledge Engineering and Discovery Research InstituteAuckland University of TechnologyAucklandNew Zealand

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