Symbolic Translation of Time Series using Piecewise N-gram Similarity Voting - Archive ouverte HAL Access content directly
Conference Papers Year : 2021

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

Abstract

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 (ORI-ENTOI) 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.
Fichier principal
Vignette du fichier
103176.pdf (365.91 Ko) Télécharger le fichier
Origin Publisher files allowed on an open archive

Dates and versions

hal-03480963 , version 1 (15-12-2021)

Identifiers

Cite

Siegfried Delannoy, Émilie Poisson Caillault, André Bigand, Kevin Rousseeuw. Symbolic Translation of Time Series using Piecewise N-gram Similarity Voting. ICEPRAM 2021 - 10th International Conference on Pattern Recognition Applications and Methods, Feb 2021, Online Streaming, France. pp.327-333, ⟨10.5220/0010317603270333⟩. ⟨hal-03480963⟩
48 View
65 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More