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Multi-Variate Time Series Similarity Measures and Their Robustness Against Temporal Asynchrony

Abstract The amount of time series data generated is increasing due to the integration of sensor technologies with everyday applications, such as gesture recognition, energy optimization, health care, video surveillance. The use of multiple sensors simultaneously

for capturing different aspects of the real world attributes has also led to an increase in dimensionality from uni-variate to multi-variate time series. This has facilitated richer data representation but also has necessitated algorithms determining similarity between two multi-variate time series for search and analysis.

Various algorithms have been extended from uni-variate to multi-variate case, such as multi-variate versions of Euclidean distance, edit distance, dynamic time warping.... (more)
Created Date 2015
Contributor Garg, Yash (Author) / Candan, Kasim Selcuk (Advisor) / Chowell-Punete, Gerardo (Committee member) / Tong, Hanghang (Committee member) / Davulcu, Hasan (Committee member) / Sapino, Maria Luisa (Committee member) / Arizona State University (Publisher)
Subject Computer science / asynchrony / correlation / metadata / multi-variate / similarity / time series
Type Masters Thesis
Extent 122 pages
Language English
Reuse Permissions All Rights Reserved
Note Masters Thesis Computer Science 2015
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis