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Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Online evolving fuzzy rule-based prediction model for high frequency trading financial data stream
AU - Gu, Xiaowei
AU - Angelov, Plamen Parvanov
AU - Mohd Ali, Azliza
AU - Gruver, William A.
AU - Gaydadjiev, Georgi
N1 - ©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.
PY - 2016/5/25
Y1 - 2016/5/25
N2 - 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.
AB - 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.
KW - online learning
KW - online prediction
KW - fuzzy rule based systems
KW - high frequency financial data stream
KW - recursively updating
KW - data density
U2 - 10.1109/EAIS.2016.7502509
DO - 10.1109/EAIS.2016.7502509
M3 - Conference contribution/Paper
SP - 169
EP - 175
BT - Evolving and Adaptive Intelligent Systems (EAIS), 2016 IEEE Conference on
PB - IEEE
T2 - The 2016 IEEE Conference on Evolving and Adaptive Intelligent Systems
Y2 - 25 May 2016
ER -