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Permanent link (DOI): https://doi.org/10.7939/R3G15TN4T

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Time Series Contextual Anomaly Detection for Detecting Stock Market Manipulation Open Access

Descriptions

Other title
Subject/Keyword
anomaly detection
time series
market manipulation
stock market
sentiment analysis
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Golmohammadi, Seyed Koosha
Supervisor and department
Zaiane, Osmar R (Computing Science)
Examining committee member and department
Nascimento, Mario (Computing Science)
Watanabe, Masahiro (Business)
Reformat, Marek (Electrical and Computer Engineering)
Leung, Carson Kai-Sang (Computer Science)
Department
Department of Computing Science
Specialization

Date accepted
2016-09-28T11:50:11Z
Graduation date
2016-06:Fall 2016
Degree
Doctor of Philosophy
Degree level
Doctoral
Abstract
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.
Language
English
DOI
doi:10.7939/R3G15TN4T
Rights
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. 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.
Citation for previous publication
Koosha Golmohammadi and Osmar R Zaiane. Data mining applications for fraud detection in securities market. In Intelligence and Security Informatics Conference (EISIC), 2012 European, pages 107-114. IEEE, 2012Koosha Golmohammadi and Osmar R Zaiane. Time series contextual anomaly detection for detecting market manipulation in stock market. In The 2015 Data Science and Advanced Analytics (DSAA'2015), pages 1-10. IEEE, 2015Koosha Golmohammadi, Osmar R Zaiane, and David Diaz. Detecting stock market manipulation using supervised learning algorithms. In The 2014 International Conference on Data Science and Advanced Analytics (DSAA'2014), pages 435-441. IEEE, 2014

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