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Leveraging Metadata for Extracting Robust Multi-Variate Temporal Features


Abstract In recent years, there are increasing numbers of applications that use multi-variate time series data where multiple uni-variate time series coexist. However, there is a lack of systematic of multi-variate time series. This thesis focuses on (a) defining a simplified inter-related multi-variate time series (IMTS) model and (b) developing robust multi-variate temporal (RMT) feature extraction algorithm that can be used for locating, filtering, and describing salient features in multi-variate time series data sets. The proposed RMT feature can also be used for supporting multiple analysis tasks, such as visualization, segmentation, and searching / retrieving based on multi-variate time series similarities. Experiments confirm that the propose... (more)
Created Date 2013
Contributor Wang, Xiaolan (Author) / Candan, Kasim Selcuk (Advisor) / Sapino, Maria Luisa (Committee member) / Fainekos, Georgios (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Subject Computer science / IMTS model / Multi-variate time series / RMT feature
Type Masters Thesis
Extent 79 pages
Language English
Rights All Rights Reserved
Note M.S. Computer Science 2013
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis