Symbolic Translation of Time Series using Piecewise N-gram Similarity Voting

This paper studies a way to discriminate user behaviour from their viewed pages in a web-application. This technique is on similarity measure selection and time sequence splitting techniques. Using temporal splitting techniques, the proposed similarity measures greatly improve the result accuracy. We applied these ones on several datasets from the well known UCR Archive and our research is focused on a private dataset (ORIENTOI) and a public one called UCR-CBF. Some of the proposed temporal tricks appear to make similarity measures efficient with noises. They make them possible to deal with repeating terms, which is a drawback for most of the similarity measures. Thus the similarity measures are shown to reach the state of the art on UCR datasets. We also evaluated the proposed technique on our private (ORIENTOI) dataset with success. We finally discuss about the weakness of our method and the ways to improve it.