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.
CITATION STYLE
Gay, D., Bondu, A., Lemaire, V., Boullé, M., & Clérot, F. (2020). Multivariate time series classification: A relational way. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12393 LNCS, pp. 316–330). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-59065-9_25
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