Outlier Mining on Multiple Time Series Data in Stock Market

Authors:
Chao Luo;Yanchang Zhao;Longbing Cao;Yuming Ou;Li Liu
Affiliations:
Faculty of Engineering & IT, University of Technology, Sydney, Australia;Faculty of Engineering & IT, University of Technology, Sydney, Australia;Faculty of Engineering & IT, University of Technology, Sydney, Australia;Faculty of Engineering & IT, University of Technology, Sydney, Australia;Faculty of Engineering & IT, University of Technology, Sydney, Australia
Venue:
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Year:
2008

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Abstract

With the dramatic increase of stock market data, traditional outlier mining technologies have shown their limitations in efficiency and precision. In this paper, an outlier mining model on stock market data is proposed, which aims to detect the anomalies from multiple complex stock market data. This model is able to improve the precision of outlier mining on individual time series. The experiments on real-world stock market data show that the proposed outlier mining model is effective and outperforms traditional technologies.