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

Gu, Xiaowei and Angelov, Plamen Parvanov and Mohd Ali, Azliza and Gruver, William A. and Gaydadjiev, Georgi (2016) Online evolving fuzzy rule-based prediction model for high frequency trading financial data stream. In: Evolving and Adaptive Intelligent Systems (EAIS), 2016 IEEE Conference on :. IEEE, BRA, pp. 169-175. ISBN 9781509025831

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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.

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?? online learningonline predictionfuzzy rule based systemshigh frequency financial data streamrecursively updatingdata density ??
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30 Aug 2016 09:20
Last Modified:
16 Jul 2024 03:53