A non-time series approach to vehicle related time series problems
Jonathan R. Wells, Kai Ming Ting, Chandrasiri P. Naiwala · FedUni ResearchOnline (Federation University Australia) · 2012
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.