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2020 | OriginalPaper | Chapter

Multivariate Time Series Classification: A Relational Way

Authors : Dominique Gay, Alexis Bondu, Vincent Lemaire, Marc Boullé, Fabrice Clérot

Published in: Big Data Analytics and Knowledge Discovery

Publisher: Springer International Publishing

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Abstract

Multivariate Time Series Classification (MTSC) has attracted increasing research attention in the past years due to the wide range applications in e.g., action/activity recognition, EEG/ECG classification, etc. In this paper, we open a novel path to tackle with MTSC: a relational way. The multiple dimensions of MTS are represented in a relational data scheme, then a propositionalisation technique (based on classical aggregation/selection functions from the relational data field) is applied to build interpretable features from secondary tables to “flatten” the data. Finally, the MTS flattened data are classified using a selective Naïve Bayes classifier. Experimental validation on various benchmark data sets show the relevance of the suggested approach.

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previous chapter FIBS: A Generic Framework for Classifying Interval-Based Temporal Sequences

next chapter Behave or Be Detected! Identifying Outlier Sequences by Their Group Cohesion

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Title: Multivariate Time Series Classification: A Relational Way
Authors: Dominique Gay
Alexis Bondu
Vincent Lemaire
Marc Boullé
Fabrice Clérot
Publisher: Springer International Publishing
Book: Big Data Analytics and Knowledge Discovery
Print ISBN: 978-3-030-59064-2

Electronic ISBN: 978-3-030-59065-9

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-59065-9_25

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