@inproceedings{7071378ec53e4012be46f8ab3c9fb5bd,
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 P.\} and Ali, \{Azliza Mohd\} and Gruver, \{William A.\} and Georgi Gaydadjiev",
year = "2016",
month = jun,
day = "30",
doi = "10.1109/EAIS.2016.7502509",
language = "English",
series = "Proceedings of the 2016 IEEE Conference on Evolving and Adaptive Intelligent Systems, EAIS 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
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",
note = "10th IEEE Conference on Evolving and Adaptive Intelligent Systems, EAIS 2016 ; Conference date: 23-05-2016 Through 25-05-2016",
}