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

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

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

15 Citations (Scopus)

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
PublisherIEEE
Pages169-175
Number of pages7
ISBN (Electronic)9781509025831
DOIs
Publication statusPublished - 30 Jun 2016
Externally publishedYes
Event10th IEEE Conference on Evolving and Adaptive Intelligent Systems, EAIS 2016 - Natal, Brazil
Duration: 23 May 201625 May 2016

Conference

Conference10th IEEE Conference on Evolving and Adaptive Intelligent Systems, EAIS 2016
Country/TerritoryBrazil
CityNatal
Period23/05/1625/05/16

Keywords

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

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