Outlier Mining on Multiple Time Series Data in Stock Market

In stock market, the key surveillance function is identifying market anomalies, such as insider trading and market manipulation, to provide a fair and efficient trading platform [2,6]. Insider trading refers to the trades on privileged information unavailable to the public [8]. Market manipulation refers to the trade or action which aims to interfere with the demand or supply of a given stock to make the price increase or decrease in a particular way [3]. Recently, new intelligent technologies are required to deal with the challenges of the rapid increase of stock data. Outlier mining technologies have been used to detect market manipulation and insider trading . The objective of outlier mining is to find the data objects which are grossly different from or inconsistent with the majority of data. However, in stock market data, outliers are highly intermixed with normal data [4] and it is difficult to judge whether an object is an outlier or not. Therefore, a more effective and more efficient approach is in demand. This paper presents a new technique for outlier detection on multiple time series data in stock market. At first, principal curve algorithm is used to detect the outliers from individual measurements of stock market. Then, the generated outliers are measured with the probability of being real alerts. To improve the accuracy and precision, these outliers are combined by some rules associated with the domain knowledge. The experimental results on real stock market data show that the proposed model is feasible in practice and achieves a higher accuracy and precision than traditional methods