Time Series Discords

  • Author / Creator
    Mueller, David A.
  • Time series discords, as introduced in by Keogh et al. [5] is described as the subsequence in the time series which is maximally different from the rest of the subsequences. Discovery of time series discords has been applied to several diverse domains including space shuttle telemetry, industry, and medicine [5] to detect anomalies in the data which can identify equipment failure, unusual patterns of activity and health problems. In this thesis we will examine the problem of finding time series discords, with detailed analysis of the problem and analysis of the effectiveness of prior work. Three different areas of discord discovery will be examined: Top Discord, Variable Length Discords, and Top-K Discords. In each of these areas, we strive to reduce the number or ease the selection of input parameters required by the end user. Emphasis is also placed on improved runtime and scalability of discord discovery methods.

  • Subjects / Keywords
  • Graduation date
  • Type of Item
  • Degree
    Master of Science
  • DOI
  • 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
  • Institution
    University of Alberta
  • Degree level
  • Department
    • Department of Computing Science
  • Supervisor / co-supervisor and their department(s)
    • Parsons, Ian (Computing Science)
    • Sander, Joerg (Computing Science)
  • Examining committee members and their departments
    • Rafiei, Davood (Computing Science)
    • Sander, Joerg (Computing Science)
    • Parsons, Ian (Computing Science)