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Dynamic Aperiodic Neural Network For Time Series Prediction

Author

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  • Chiu-Che Tseng

    (Department of Computer Science and Information Systems Texas A&M University-Commerce)

Abstract

There are many things that humans find easy to do that computers are currently unable to do. Tasks such as visual pattern manipulating objects by touch, and navigating in a complex world are easy for humans. Yet, despite decades of research, we have no viable algorithms for performing these and other cognitive functions on a computer. In this study, we used a bio-inspired neural network called a KAset neural network to perform a time series predictive task. The results from our experiments showed that the predictive accuracy with this method was better in most markets than results obtained using a random walk method.

Suggested Citation

  • Chiu-Che Tseng, 2007. "Dynamic Aperiodic Neural Network For Time Series Prediction," Portuguese Journal of Management Studies, ISEG, Universidade de Lisboa, vol. 0(2), pages 99-113.
    • Handle: RePEc:pjm:journl:v:xii:y:2007:i:2:p:99-113
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    References listed on IDEAS

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    1. John Moody & Lizhong Wu, "undated". "Optimization of Trading Systems and Portfolios," Computing in Economics and Finance 1997 55, Society for Computational Economics.
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