Return to search

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 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

Identiferoai:union.ndltd.org:asu.edu/item:18794
Date January 2013
ContributorsWang, Xiaolan (Author), Candan, Kasim Selcuk (Advisor), Sapino, Maria Luisa (Committee member), Fainekos, Georgios (Committee member), Davulcu, Hasan (Committee member), Arizona State University (Publisher)
Source SetsArizona State University
LanguageEnglish
Detected LanguageEnglish
TypeMasters Thesis
Format79 pages
Rightshttp://rightsstatements.org/vocab/InC/1.0/, All Rights Reserved

Page generated in 0.0016 seconds