Personalised modelling for multiple time-series data prediction

Date
2008
Authors
Widiputra, H
Pears, R
Kasabov, N
Supervisor
Item type
Conference Contribution
Degree name
Journal Title
Journal ISSN
Volume Title
Publisher
Springer-Verlag
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.

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Source
15th International Conference on Neural Information Processing of the Asia-Pacific Neural Network Assembly, 2008 held at Sky City Convention Centre, Auckland, New Zealand, 2008-11-25 to 2008-11-28, vol.1(1), pp.1237 - 1244 (8)
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