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The Multi-Agent System for Prediction of Financial Time Series

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Artificial Intelligence and Soft Computing – ICAISC 2006 (ICAISC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4029))

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Abstract

To take into account different character of distinct segments of non-stationary financial time series the multi-agent system based forecasting algorithm is suggested. The primary goal of present paper is to introduce methodological findings that could help to reduce one step ahead forecasting error. In contrast to previous investigation [6], instead of single prediction rule we use a system of several adaptive forecasting agents. The agents evolve, compete among themselves. Final decision is made by a collective of the most successive agents and present time moment. New multi-agent forecasting system allows utilizing shorter training sequences and results in more accurate forecasts than employing single prediction algorithm.

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Author information

Authors and Affiliations

  1. Institute of Mathematics and Informatics, Dept. of Informatics, MIF, Vilnius University, Estland

    Šarūnas Raudys

  2.  , Naugarduko st. 24, Vilnius, 03225, Lithuania

    Indre Zliobaite

Authors
  1. Šarūnas Raudys
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  2. Indre Zliobaite
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Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Academy of Humanities and Economics, Poland

    Leszek Rutkowski

  2. Institute of Automatics, AGH University of Science and Technology, Al. Mickiewicza 30, PL-30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Department of Electrical Engineering and Computer Sciences, Berkeley Initiative in Soft Computing (BISC), University of California, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Żurada

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© 2006 Springer-Verlag Berlin Heidelberg

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Raudys, Š., Zliobaite, I. (2006). The Multi-Agent System for Prediction of Financial Time Series. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2006. ICAISC 2006. Lecture Notes in Computer Science(), vol 4029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11785231_68

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  • DOI: https://doi.org/10.1007/11785231_68

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-35748-3

  • Online ISBN: 978-3-540-35750-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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