Inferring structure from multivariate time series sensor data
Abstract/Contents
- Abstract
- Many applications, ranging from automobiles to financial markets, generate large amounts of time series data. In most cases, this data is multivariate and heterogeneous, where the readings come from various types of entities, or sensors. These time series datasets are often sparse, unlabeled, dynamic, and difficult to interpret. Therefore, there is a need for methods that learn interpretable structure from such data, especially for methods that can apply across many different domains. Here, we develop three novel optimization methods which can be used for analyzing temporal patterns, identifying outliers and regime changes, and segmenting a time series into a sequence of repeated states in an unsupervised way. Such methods are based on inferring the correlation structure between the different sensors, both instantaneous and across timestamps, as well as how this structure evolves over time. Using these methods, we analyze applications in various real-world domains, both in and beyond time series datasets.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2018; ©2018 |
| Publication date | 2018; 2018 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Hallac, David Philip |
|---|---|
| Degree supervisor | Leskovec, Jurij |
| Thesis advisor | Leskovec, Jurij |
| Thesis advisor | Boyd, Stephen P |
| Thesis advisor | Garcia-Molina, Hector |
| Degree committee member | Boyd, Stephen P |
| Degree committee member | Garcia-Molina, Hector |
| Associated with | Stanford University, Department of Electrical Engineering. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | David Philip Hallac. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2018. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2018 by David Philip Hallac
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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