Online evolving fuzzy rule-based prediction model for high frequency trading financial data stream

Xiaowei Gu, Plamen Parvanov Angelov, Azliza Mohd Ali, William A. Gruver, Georgi Gaydadjiev

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

Abstract

Analyzing and predicting the high frequency trading (HFT) financial data stream is very challenging due to the fast arrival times and large amount of the data samples. Aiming at solving this problem, an online evolving fuzzy rule-based prediction model is proposed in this paper. Because this prediction model is based on evolving fuzzy rule-based systems and a novel, simpler form of data density, it can autonomously learn from the live data stream, automatically build/remove its rules and recursively update the parameters. This model responds quickly to all unpredictable sudden changes of financial data and re-adjusts itself to follow the new data pattern. Experimental results show the excellent prediction performance of the proposed approach with real financial data stream regardless of quick shifts of data patterns and frequent appearances of abnormal data samples.
Original languageEnglish
Title of host publicationProceedings of the 2016 IEEE Conference on Evolving and Adaptive Intelligent Systems, EAIS 2016
EditorsBruno Sielly Jales Costa, Edwin Lughofer, Igor Skrjanc
PublisherIEEE Press
Pages169-175
Number of pages7
ISBN (Electronic)9781509025831
ISBN (Print)9781509025831
DOIs
Publication statusPublished - 04 Jul 2016
Externally publishedYes

Publication series

NameProceedings of the 2016 IEEE Conference on Evolving and Adaptive Intelligent Systems, EAIS 2016

Keywords

  • Data density
  • Fuzzy rule based systems
  • High frequency financial data stream
  • Online learning
  • Online prediction
  • Recursively updating

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