A non-time series approach to vehicle related time series problems

Authors:
Jonathan R. Wells;Kai Ming Ting;Chandrasiri P. Naiwala
Affiliations:
Monash University, Australia;Monash University, Australia;Toyota InfoTechnology Center Co., Ltd., Japan
Venue:
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
Year:
2012

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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.