Thesis
Enhancing smart home resident activity prediction and anomaly detection using temporal relations
Washington State University
Master of Science (MS), Washington State University
2007
Handle:
https://hdl.handle.net/2376/103173
Abstract
Technological enhancements aid development and research in smart homes and intelligent environments. The temporal nature of data collected in a smart environment provides us with a better understanding of patterns that occur over time. Predicting events and detecting anomalies in such datasets is a complex and challenging task. To solve this problem, we suggest a solution using temporal relations. Our temporal pattern discovery algorithm, based on Allen's temporal relations, has helped discover interesting patterns and relations on smart home datasets. We hypothesize that machine learning algorithms can be designed to automatically learn models of resident behavior in a smart home, and when these are incorporated with temporal information, the results can be used to enhance prediction and to detect anomalies. We describe a method of discovering temporal relations in smart home datasets and applying them to perform anomaly detection on the frequently-occurring events and enhance sequential prediction by incorporating temporal relation information shared by the activity. We validate our hypothesis using empirical studies based on the data collected from real resident and virtual resident (or synthetic) data.
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Details
- Title
- Enhancing smart home resident activity prediction and anomaly detection using temporal relations
- Creators
- Vikramaditya Reddy Jakkula
- Contributors
- Diane J. Cook (Degree Supervisor)
- Awarding Institution
- Washington State University
- Academic Unit
- Electrical Engineering and Computer Science, School of
- Theses and Dissertations
- Master of Science (MS), Washington State University
- Publisher
- Washington State University; [Pullman, Washington] :
- Identifiers
- 99900525084301842
- Language
- English
- Resource Type
- Thesis