Title :
Exception Mining on Multiple Time Series in Stock Market
Author :
Luo, Chao ; Zhao, Yanchang ; Cao, Longbing ; Ou, Yuming ; Zhang, Chengqi
Author_Institution :
Fac. of Eng. & Inf. Technol., Univ. of Technol., Sydney, NSW
Abstract :
This paper presents our research on exception mining on multiple time series data which aims to assist stock market surveillance by identifying market anomalies. Traditional technologies on stock market surveillance have shown their limitations to handle large amount of complicated stock market data. In our research, the outlier mining on multiple time series (OMM) is proposed to improve the effectiveness of exception detection for stock market surveillance. The idea of our research is presented, challenges on the research are analyzed, and potential research directions are summarized.
Keywords :
stock markets; time series; exception detection; exception mining; market anomalies; multiple time series; outlier mining; stock market surveillance; Chaos; Data engineering; Data mining; Humans; Information technology; Intelligent agent; Stock markets; Surveillance; Time measurement; Volume measurement;
Conference_Titel :
Web Intelligence and Intelligent Agent Technology, 2008. WI-IAT '08. IEEE/WIC/ACM International Conference on
Conference_Location :
Sydney, NSW
Print_ISBN :
978-0-7695-3496-1
DOI :
10.1109/WIIAT.2008.302