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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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
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