@inproceedings{74665a915dce4342a4f729ce7a959d92,
title = "Online evolving fuzzy rule-based prediction model for high frequency trading financial data stream",
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.",
keywords = "Data density, Fuzzy rule based systems, High frequency financial data stream, Online learning, Online prediction, Recursively updating",
author = "Xiaowei Gu and Angelov, {Plamen Parvanov} and Ali, {Azliza Mohd} and Gruver, {William A.} and Georgi Gaydadjiev",
note = "Funding Information: This work was supported by The Royal Society (Grant number IE141329/2014). This paper is dedicated to the memory of William A. Gruver, who passed away on Feb. 29, 2016. Dr. Gruver was a pioneer of the study of robotics and intelligent systems, IEEE fellow, laboratory head of Intelligent/Distributed Enterprise Automation Laboratory and professor emeritus of School of Engineering Science at Simon Fraser University, Canada. Publisher Copyright: {\textcopyright}2016 IEEE.",
year = "2016",
month = jul,
day = "4",
doi = "10.1109/EAIS.2016.7502509",
language = "English",
isbn = "9781509025831",
series = "Proceedings of the 2016 IEEE Conference on Evolving and Adaptive Intelligent Systems, EAIS 2016",
publisher = "IEEE Press",
pages = "169--175",
editor = "Costa, {Bruno Sielly Jales} and Edwin Lughofer and Igor Skrjanc",
booktitle = "Proceedings of the 2016 IEEE Conference on Evolving and Adaptive Intelligent Systems, EAIS 2016",
address = "United States of America",
}