Time Series Contextual Anomaly Detection for Detecting Market Manipulation in Stock Market

Anomaly detection in time series is one of the fundamental issues in data mining that addresses various problems in different domains such as intrusion detection in computer networks, irregularity detection in healthcare sensory data and fraud detection in insurance or securities. Although, there has been extensive work on anomaly detection, majority of the techniques look for individual objects that are different from normal objects but do not take the temporal aspect of data into consideration. We are particularly interested in contextual outlier detection methods for time series that are applicable to fraud detection in securities. This has significant impacts on national and international securities markets. In this paper, we propose a prediction-based Contextual Anomaly Detection (CAD) method for complex time series that are not described through deterministic models. The proposed method improves the recall from 7% to 33% compared to kNN and Random Walk without compromising the precision.