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2012 | OriginalPaper | Chapter

12. Dynamic Learning of Multiple Time Series in a Nonstationary Environment

Authors : Harya Widiputra, Russel Pears, Nikola Kasabov

Published in: Learning in Non-Stationary Environments

Publisher: Springer New York

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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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Title: Dynamic Learning of Multiple Time Series in a Nonstationary Environment
Authors: Harya Widiputra
Russel Pears
Nikola Kasabov
Publisher: Springer New York
Book: Learning in Non-Stationary Environments
Print ISBN: 978-1-4419-8019-9

Electronic ISBN: 978-1-4419-8020-5

Copyright Year: 2012
DOI: https://doi.org/10.1007/978-1-4419-8020-5_12

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