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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

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.

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    Date Created
    • 2013
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  • Text
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    Note
    • Partial requirement for: M.S., Arizona State University, 2013
      Note type
      thesis
    • Includes bibliographical references (p. 65-69)
      Note type
      bibliography
    • Field of study: Computer science

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    by Xiaolan Wang

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