Personalised modelling for multiple time-series data prediction: a preliminary investigation in Asia Pacific stock market indexes movement

Authors:
Harya Widiputra;Russel Pears;Nikola Kasabov
Affiliations:
Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland, New Zealand;Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland, New Zealand;Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland, New Zealand
Venue:
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Year:
2008

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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.