nach oben

Erschienen in:

2020 | OriginalPaper | Buchkapitel

Multivariate Time Series Classification: A Relational Way

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

Erschienen in: Big Data Analytics and Knowledge Discovery

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel FIBS: A Generic Framework for Classifying Interval-Based Temporal Sequences

Nächstes Kapitel Behave or Be Detected! Identifying Outlier Sequences by Their Group Cohesion

Bagnall, A.J., et al.: The UEA multivariate time series classification archive, 2018. CoRR abs/1811.00075 (2018). http://timeseriesclassification.com

Bagnall, A.J., Davis, L.M., Hills, J., Lines, J.: Transformation based ensembles for time series classification. In: Proceedings of the Twelfth SIAM International Conference on Data Mining, (SDM 2012), Anaheim, California, USA, 26–28 April 2012, pp. 307–318 (2012)

Bagnall, A., Lines, J., Bostrom, A., Large, J., Keogh, E.: The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Min. Knowl. Disc. 31(3), 606–660 (2016). https://doi.org/10.1007/s10618-016-0483-9MathSciNetCrossRef

Baydogan, M.G.: Multivariate time series classification data sets (2019). http://www.mustafabaydogan.com

Baydogan, M.G., Runger, G.: Learning a symbolic representation for multivariate time series classification. Data Min. Knowl. Disc. 29(2), 400–422 (2014). https://doi.org/10.1007/s10618-014-0349-yMathSciNetCrossRefMATH

Baydogan, M.G., Runger, G.: Time series representation and similarity based on local autopatterns. Data Min. Knowl. Disc. 30(2), 476–509 (2015). https://doi.org/10.1007/s10618-015-0425-yMathSciNetCrossRefMATH

Bondu, A., Gay, D., Lemaire, V., Boullé, M., Cervenka, E.: FEARS: a feature and representation selection approach for time series classification. In: Proceedings of The 11th Asian Conference on Machine Learning, ACML 2019, Nagoya, Japan, 17–19 November 2019, pp. 379–394 (2019)

Boullé, M.: MODL: a Bayes optimal discretization method for continuous attributes. Mach. Learn. 65(1), 131–165 (2006)CrossRef

Boullé, M.: Compression-based averaging of selective Naive Bayes classifiers. J. Mach. Learn. Res. 8, 1659–1685 (2007)MathSciNetMATH

10.

Boullé, M., Charnay, C., Lachiche, N.: A scalable robust and automatic propositionalization approach for Bayesian classification of large mixed numerical and categorical data. Mach. Learn. 108(2), 229–266 (2019)MathSciNetCrossRef

11.

Cuturi, M., Doucet, A.: Autoregressive kernels for time series. CoRR abs/1101.0673 (2011). https://arxiv.org/abs/1101.0673

12.

Demšar, J.: Statistical comparisons of classifiers over multiple data sets. JMLR 7, 1–30 (2006)MathSciNetMATH

13.

Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., Muller, P.-A.: Deep learning for time series classification: a review. Data Min. Knowl. Disc. 33(4), 917–963 (2019). https://doi.org/10.1007/s10618-019-00619-1MathSciNetCrossRef

14.

Hsu, E.-Y., Liu, C.-L., Tseng, V.S.: Multivariate time series early classification with interpretability using deep learning and attention mechanism. In: Yang, Q., Zhou, Z.-H., Gong, Z., Zhang, M.-L., Huang, S.-J. (eds.) PAKDD 2019. LNCS (LNAI), vol. 11441, pp. 541–553. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-16142-2_42CrossRef

15.

Karim, F., Majumdar, S., Darabi, H., Harford, S.: Multivariate LSTM-FCNS for time series classification. Neural Netw. 116, 237–245 (2019)CrossRef

16.

Karlsson, I., Papapetrou, P., Boström, H.: Generalized random shapelet forests. Data Min. Knowl. Disc. 30(5), 1053–1085 (2016). https://doi.org/10.1007/s10618-016-0473-yMathSciNetCrossRefMATH

17.

Lachiche, N.: Propositionalization. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning and Data Mining, pp. 1025–1031. Springer, Boston (2017). https://doi.org/10.1007/978-1-4899-7687-1_686CrossRef

18.

Lines, J., Taylor, S., Bagnall, A.J.: Time series classification with HIVE-COTE: the hierarchical vote collective of transformation-based ensembles. ACM Trans. Knowl. Disc. Data 12(5), 52:1–52:35 (2018)

19.

Schäfer, P., Leser, U.: Fast and accurate time series classification with WEASEL. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, CIKM 2017, Singapore, 06–10 November 2017, pp. 637–646 (2017)

20.

Schäfer, P., Leser, U.: Multivariate time series classification with WEASEL+MUSE. CoRR abs/1711.11343 (2017). http://arxiv.org/abs/1711.11343

21.

Shokoohi-Yekta, M., Wang, J., Keogh, E.J.: On the non-trivial generalization of dynamic time warping to the multi-dimensional case. In: Proceedings of the 2015 SIAM International Conference on Data Mining, Vancouver, BC, Canada, 30 April–2 May 2015, pp. 289–297 (2015)

22.

Tuncel, K.S., Baydogan, M.G.: Autoregressive forests for multivariate time series modeling. Pattern Recogn. 73, 202–215 (2018)CrossRef

Titel: Multivariate Time Series Classification: A Relational Way
verfasst von: Dominique Gay
Alexis Bondu
Vincent Lemaire
Marc Boullé
Fabrice Clérot
Verlag: Springer International Publishing
Buch: Big Data Analytics and Knowledge Discovery
Print ISBN: 978-3-030-59064-2

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

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

Premium Partner

Bildnachweise

Rittal/© Rittal, NTT Data/© NTT Data, Wildix/© Wildix, Ceyoniq Technology GmbH/© Ceyoniq Technology GmbH, arvato Systems GmbH/© arvato Systems GmbH, Ninox Software GmbH/© Ninox Software GmbH, Everyday Software S.L./© Everyday Software S.L., Redgate/© Redgate, CELONIS Labs GmbH, DC-Datacenter-Group GmbH/© DC-Datacenter-Group GmbH, all for one/© all for one, G Data CyberDefense/© G Data CyberDefense, FAST LTA/© FAST LTA, Vendosoft/© Vendosoft, Kumavision/© Kumavision, Noriis Network AG/© Noriis Network AG, WSW Software GmbH/© WSW Software GmbH

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"