Leveraging Metadata for Extracting Robust Multi-Variate Temporal Features

• Other Authors: Wang, Xiaolan (Author)
• Format: Dissertation
• Language: English
• Published: 2013
• Subjects: Computer science; IMTS model; Multi-variate time series; RMT feature
• Online Access: http://hdl.handle.net/2286/R.I.18794

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