Frequent Knowledge Patterns in Evolutionary Decision Support Systems for Financial Time Series Analysis

  • Piotr Lipinski
Part of the Studies in Computational Intelligence book series (SCI, volume 293)


This chapter discusses extracting and reusing frequent knowledge patterns in building trading experts in an evolutionary decision support system for financial time series analysis. It focuses on trading experts built by an evolutionary algorithm as binary sequences representing subsets of a specific set of trading rules, where frequent knowledge patterns correspond to common building blocks of trading rules occurring in previous trading experts. Reusing frequent knowledge patterns leads to a significant reduction of the search space, due to fixing a part of chromosome and running the evolution process to set only the remaining genes, without significant decreases of results.

This chapter presents a number of experiments carried out on financial time series from the Paris Stock Exchange, discusses some examples of the frequent knowledge patterns as well as analyses the results obtained in terms of their financial relevance and compares them with some popular benchmarks.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Piotr Lipinski
    • 1
  1. 1.Institute of Computer ScienceUniversity of WroclawWroclawPoland

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