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

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

Published
Publication date25/05/2016
Host publicationEvolving and Adaptive Intelligent Systems (EAIS), 2016 IEEE Conference on
PublisherIEEE
Pages169-175
Number of pages7
ISBN (Electronic)9781509025831
Original languageEnglish
EventThe 2016 IEEE Conference on Evolving and Adaptive Intelligent Systems - , Brazil
Duration: 25/05/2016 → …

Conference

ConferenceThe 2016 IEEE Conference on Evolving and Adaptive Intelligent Systems
CountryBrazil
Period25/05/16 → …

Conference

ConferenceThe 2016 IEEE Conference on Evolving and Adaptive Intelligent Systems
CountryBrazil
Period25/05/16 → …

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.

Bibliographic note

©2016 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.