Anomaly detection of time series.
2010-05
Loading...
View/Download File
Persistent link to this item
Statistics
View StatisticsJournal Title
Journal ISSN
Volume Title
Title
Anomaly detection of time series.
Authors
Published Date
2010-05
Publisher
Type
Thesis or Dissertation
Abstract
This thesis deals with the problem of anomaly detection for time series data. Some of
the important applications of time series anomaly detection are healthcare, eco-system
disturbances, intrusion detection and aircraft system health management.
Although there has been extensive work on anomaly detection (1), most of the
techniques look for individual objects that are different from normal objects but do not
consider the sequence aspect of the data into consideration.
In this thesis, we analyze the state of the art of time series anomaly detection
techniques and present a survey. We also propose novel anomaly detection techniques
and transformation techniques for the time series data. Through extensive experimental
evaluation of the proposed techniques on the data sets collected across diverse domains,
we conclude that our techniques perform well across many datasets.
Description
University of Minnesota M.S. thesis. May 2010. Major: Computer Science. Advisor: Prof.Vipin Kumar. 1 computer science (PDF); vi, 75 pages. Ill. (some col.)
Related to
Replaces
License
Series/Report Number
Funding information
Isbn identifier
Doi identifier
Previously Published Citation
Suggested citation
Cheboli, Deepthi. (2010). Anomaly detection of time series.. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/92985.
Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.