Gu, Xiaowei, Angelov, Plamen P., Ali, Azliza Mohd, Gruver, William A., Gaydadjiev, Georgi (2016) Online evolving fuzzy rule-based prediction model for high frequency trading financial data stream. In: 2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS). . pp. 169-175. IEEE E-ISBN 978-1-5090-2583-1. (doi:10.1109/EAIS.2016.7502509) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:90215)
| The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. | |
| Official URL: https://doi.org/10.1109/EAIS.2016.7502509 |
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.
| Item Type: | Conference or workshop item (Paper) |
|---|---|
| DOI/Identification number: | 10.1109/EAIS.2016.7502509 |
| Uncontrolled keywords: | 5G mobile communication; Conferences; Adaptive systems; Intelligent systems; online learning; online prediction; fuzzy rule based systems; high frequency financial data stream; recursively updating; data density |
| Subjects: | Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science |
| Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing |
| Depositing User: | Amy Boaler |
| Date Deposited: | 14 Sep 2021 14:58 UTC |
| Last Modified: | 05 Nov 2024 12:55 UTC |
| Resource URI: | https://kar.kent.ac.uk/id/eprint/90215 (The current URI for this page, for reference purposes) |
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