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Neuro‐fuzzy time‐series analysis of large‐volume data

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  • Jeff Schott
  • Jugal Kalita

Abstract

This paper describes a framework that utilizes an adaptive‐network‐based fuzzy inference system to perform user‐constrained pattern recognition on time‐series data. Using a customizable fuzzy logic grammar, the architecture allows an analyst to capture domain expertise in a context‐relevant manner. Fuzzy logic rules constructed by the analyst are used to perform feature extraction and influence the training of a neural network to perform pattern recognition. We demonstrate that the architecture is capable of performing noise‐tolerant searches across multiple features on large volumes of time‐series data. The experiments presented here are from the domain of stock analysis. We are able to create simple rule sets automatically to search a data warehouse of stocks to select stocks that exhibit desirable behaviours. Copyright © 2011 John Wiley & Sons, Ltd.

Suggested Citation

  • Jeff Schott & Jugal Kalita, 2011. "Neuro‐fuzzy time‐series analysis of large‐volume data," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 18(1), pages 39-57, January.
    • Handle: RePEc:wly:isacfm:v:18:y:2011:i:1:p:39-57
    DOI: 10.1002/isaf.322
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    References listed on IDEAS

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    1. Hendry, David F. & Clements, Michael P., 2003. "Economic forecasting: some lessons from recent research," Economic Modelling, Elsevier, vol. 20(2), pages 301-329, March.
    2. Ramsey, J.B., 2002. "Wavelets in Economics and Finance: Past and Future," Working Papers 02-02, C.V. Starr Center for Applied Economics, New York University.
    3. Gary R. Weckman & Sriram Lakshminarayanan & Jon H. Marvel & Andy Snow, 2008. "An integrated stock market forecasting model using neural networks," International Journal of Business Forecasting and Marketing Intelligence, Inderscience Enterprises Ltd, vol. 1(1), pages 30-49.
    4. Ramsey James B., 2002. "Wavelets in Economics and Finance: Past and Future," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 6(3), pages 1-29, November.
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