Paper Info
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.
Year
DOI
Venue
2016
10.1109/EAIS.2016.7502509
2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)
Keywords
DocType
ISSN
online learning,online prediction,fuzzy rule based systems,high frequency financial data stream,recursively updating,data density
Conference
2330-4863
Citations 
PageRank 
References 
0
0.34
11
Authors
5
Name
Order
Citations
PageRank
Xiaowei Gu19910.96
Plamen P. Angelov247327.83
Azliza Mohd Ali321.73
William A. Gruver425349.27
Georgi Gaydadjiev51117104.92