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 proposed feature extraction algorithm is highly efficient and effective in identifying robust multi-scale temporal features of multi-variate time series. / Dissertation/Thesis / M.S. Computer Science 2013
| Identifer | oai:union.ndltd.org:asu.edu/item:18794 |
| Date | January 2013 |
| Contributors | Wang, Xiaolan (Author), Candan, Kasim Selcuk (Advisor), Sapino, Maria Luisa (Committee member), Fainekos, Georgios (Committee member), Davulcu, Hasan (Committee member), Arizona State University (Publisher) |
| Source Sets | Arizona State University |
| Language | English |
| Detected Language | English |
| Type | Masters Thesis |
| Format | 79 pages |
| Rights | http://rightsstatements.org/vocab/InC/1.0/, All Rights Reserved |
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