Leveraging metadata for extracting robust multi-variate temporal features
Description
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.
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2013
Agent
- Author (aut): Wang, Xiaolan
- Thesis advisor (ths): Candan, Kasim Selcuk
- Committee member: Sapino, Maria Luisa
- Committee member: Fainekos, Georgios
- Committee member: Davulcu, Hasan
- Publisher (pbl): Arizona State University