Exception Mining on Multiple Time Series in Stock Market

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.