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Time Series Contextual Anomaly Detection for Detecting Stock Market Manipulation

  • Author / Creator
    Golmohammadi, Seyed Koosha
  • Anomaly detection in time series is one of the fundamental issues in data mining. It addresses various problems in different domains such as intrusion detection in computer networks, anomaly detection in healthcare sensory data, and fraud detection in securities. Though there has been extensive work on anomaly detection, most 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 anomaly 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 thesis, we propose a prediction-based Contextual Anomaly Detection (CAD) method for complex time series that are not described through deterministic models. First, a subset of time series is selected based on the window size parameter, Second, a centroid is calculated representing the expected behaviour of time series of the group. Then, the centroid values are used along with correlation of each time series with the centroid to predict the values of the time series. The proposed method improves recall from 7% to 33% compared to kNN and random walk without compromising precision. We propose a formalized method to improve performance of CAD using big data techniques by eliminating false positives. The method aims to capture expected behaviour of stocks through sentiment analysis of tweets about stocks. We present a case study and explore developing sentiment analysis models to improve anomaly detection in the stock market. The experimental results confirm the proposed method is effective in improving CAD through removing irrelevant anomalies by correctly identifying 28% of false positives.

  • Subjects / Keywords
  • Graduation date
    2016-06:Fall 2016
  • Type of Item
    Thesis
  • Degree
    Doctor of Philosophy
  • DOI
    https://doi.org/10.7939/R3G15TN4T
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
    English
  • Institution
    University of Alberta
  • Degree level
    Doctoral
  • Department
    • Department of Computing Science
  • Supervisor / co-supervisor and their department(s)
    • Zaiane, Osmar R (Computing Science)
  • Examining committee members and their departments
    • Reformat, Marek (Electrical and Computer Engineering)
    • Leung, Carson Kai-Sang (Computer Science)
    • Nascimento, Mario (Computing Science)
    • Watanabe, Masahiro (Business)