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Time series contextual anomaly detection for detecting market manipulation in stock market

机译：时间序列上下文异常检测，用于检测股票市场中的市场操纵

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摘要

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.
机译：时间序列中的异常检测是数据挖掘中的基本问题之一，解决了不同领域中的各种问题，例如计算机网络中的入侵检测，医疗保健传感数据中的不规则检测以及保险或证券中的欺诈检测。尽管在异常检测方面已经进行了广泛的工作，但是大多数技术都在寻找与正常对象不同的单个对象，但并未考虑数据的时间方面。我们对适用于证券欺诈检测的时间序列的上下文离群值检测方法特别感兴趣。这对国家和国际证券市场产生重大影响。在本文中，我们针对复杂的时间序列提出了一种基于预测的上下文异常检测（CAD）方法，该方法未通过确定性模型进行描述。与kNN和Random Walk相比，该方法将召回率从7％提高到33％，而不会影响精度。

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