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Thesis.pdf (821.08 KB)
ETD Abstract Container
Abstract Header
Hierarchical Anomaly Detection for Time Series Data
Author Info
Sperl, Ryan E.
Permalink:
http://rave.ohiolink.edu/etdc/view?acc_num=wright1590709752916657
Abstract Details
Year and Degree
2020, Master of Science (MS), Wright State University, Computer Science.
Abstract
With the rise of Big Data and the Internet of Things, there is an increasing availability of large volumes of real-time streaming data. Unusual occurrences in the underlying system will be reflected in these streams, but any human analysis will quickly become out of date. There is a need for automatic analysis of streaming data capable of identifying these anomalous behaviors as they occur, to give ample time to react. In order to handle many high-velocity data streams, detectors must minimize the processing requirements per value. In this thesis, we have developed a novel anomaly detection method which makes use of a diverse set of detectors in a hierarchical structure. The composite detector follows a filtration paradigm to mark each value in the series. The base model, chosen to be fast potentially at the expense of precision, identifies candidate anomalies in the series as each value arrives. Models higher in the hierarchy verify the candidates from their immediate predecessor, potentially rejecting some as false alarms. Our experiments show that this hierarchical method can achieve similar performance to state-of-the-art detectors using computationally simple models with lower processing requirements, enabling better scalability.
Committee
Soon M. Chung, Ph.D. (Advisor)
Vincent A. Schmidt, Ph.D. (Committee Member)
Nikolaos Bourbakis, Ph.D. (Committee Member)
Pages
34 p.
Subject Headings
Computer Science
;
Information Science
Keywords
time series data
;
anomaly detection
;
moving-average
;
SARIMA
;
streaming data
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Citations
Sperl, R. E. (2020).
Hierarchical Anomaly Detection for Time Series Data
[Master's thesis, Wright State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=wright1590709752916657
APA Style (7th edition)
Sperl, Ryan.
Hierarchical Anomaly Detection for Time Series Data.
2020. Wright State University, Master's thesis.
OhioLINK Electronic Theses and Dissertations Center
, http://rave.ohiolink.edu/etdc/view?acc_num=wright1590709752916657.
MLA Style (8th edition)
Sperl, Ryan. "Hierarchical Anomaly Detection for Time Series Data." Master's thesis, Wright State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=wright1590709752916657
Chicago Manual of Style (17th edition)
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Document number:
wright1590709752916657
Download Count:
271
Copyright Info
© 2020, all rights reserved.
This open access ETD is published by Wright State University and OhioLINK.