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Dynamic Learning of Multiple Time Series in a Nonstationary Environment

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Learning in Non-Stationary Environments

Abstract

This chapter introduces two distinct solutions to the problem of capturing the dynamics of multiple time series and the extraction of useful knowledge over time. As these dynamics would change in a nonstationary environment, the key characteristic of the methods is the ability to evolve their structure continuously over time. In addition, reviews of existing methods of dynamic single time series analysis and modeling such as the dynamic neuro-fuzzy inference system and the neuro-fuzzy inference method for transductive reasoning, which inspired the proposed methods, are presented. This chapter also presents a comprehensive evaluation of the performance of the proposed methods on a real-world problem, which consists of predicting movement of global stock market indexes over time.

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Acknowledgements

The authors would like to thank the Knowledge Engineering and Discovery Research Institute (KEDRI) and all the members for their supports, constructive discussions, and inspirational ideas. The authors would also like to thank the School of Computing and Mathematical Sciences of Auckland University of Technology, New Zealand for the scholarship granted to Harya Widiputra.

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Authors and Affiliations

  1. The Knowledge Engineering and Discovery Research Institute, Level 7 350 Queen St, Auckland, New Zealand

    Harya Widiputra, Russel Pears & Nikola Kasabov

Authors
  1. Harya Widiputra
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  2. Russel Pears
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  3. Nikola Kasabov
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Corresponding author

Correspondence to Harya Widiputra .

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Editors and Affiliations

  1. , Départment Informatique et Automatique, Ecole des Mines de Douai, 941, Rue Charles Bourseul, Douai cedex, 59508, France

    Moamar Sayed-Mouchaweh

  2. University of Linz, Weissdornweg 16, Linz, 4232, Austria

    Edwin Lughofer

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Widiputra, H., Pears, R., Kasabov, N. (2012). Dynamic Learning of Multiple Time Series in a Nonstationary Environment. In: Sayed-Mouchaweh, M., Lughofer, E. (eds) Learning in Non-Stationary Environments. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-8020-5_12

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  • DOI: https://doi.org/10.1007/978-1-4419-8020-5_12

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  • Print ISBN: 978-1-4419-8019-9

  • Online ISBN: 978-1-4419-8020-5

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