- Title
- A non-time series approach to vehicle related time series problems
- Creator
- Wells, Jonathan; Ting, Kaiming; Naiwala, Chandrasiri
- Date
- 2012
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/160025
- Identifier
- vital:12102
- Identifier
- ISBN:1445-1336 (ISSN); 978-1-921770-14-2 (ISBN)
- Abstract
- This paper shows that some time series problems can be better served as non-time series problems. We used two unsupervised learning anomaly detectors to analyse a vehicle related time series problem and showed that non-time series treatment produced a better outcome than a time series treatment. We also present the benefits of using unsupervised methods over semi-supervised or supervised learning methods, and rule-based methods.
- Publisher
- Australian Computer Society, Inc
- Relation
- 10th Australasian Data Mining Conference (AusDM 2012); Sydney, Australia; 5th-7th December 2012; published in Conferences in Research and Practice in Information Technology (CRPIT) Vol. 134, p. 61-70
- Rights
- Copyright © 2012, Australian Computer Society, Inc. This paper appeared at the 10th Australasian Data Mining Conference (AusDM 2012), Sydney, Australia, December 2012. Conferences in Research and Practice in Information Technology (CRPIT), Vol. 134, Yanchang Zhao, Jiuyong Li, Paul Kennedy, and Peter Christen, Ed. Reproduction for academic, not-for-profit purposes permitted provided this text is included.
- Rights
- This metadata is freely available under a CCO license
- Subject
- Non-time series; Vehicle
- Full Text
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