Abstract
Time Series (TS) clustering is one of the most effervescent research fields due to the Big Data and the IoT explosion. The problem gets more challenging if we consider the multivariate TS. In the field of Business and Management, multivariate TS are becoming more and more interesting as they allow to match events the co-occur in time but that is hardly noticeable. In this study, Recurrent Neural Networks and transfer learning have been used to analyze each example, measuring similarities between variables. All the results are finally aggregated to create an adjacency matrix that allows extracting the groups. Proof-of-concept experimentation has been included, showing that the solution might be valid after several improvements.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aghabozorgi, S., Shirkhorshidi, A.S., Wah, T.Y.: Time-series clustering - a decade review. Inf. Syst. 53, 16–38 (2015). http://www.sciencedirect.com/science/article/pii/S0306437915000733
Bode, G., Schreiber, T., Baranski, M., Müller, D.: A time series clustering approach for building automation and control systems. Appl. Energy 238, 1337–1345 (2019). http://www.sciencedirect.com/science/article/pii/S0306261919302089
Duan, L., Yu, F., Pedrycz, W., Wang, X., Yang, X.: Time-series clustering based on linear fuzzy information granules. Appl. Soft Comput. 73, 1053–1067 (2018). http://www.sciencedirect.com/science/article/pii/S1568494618305490
D’Urso, P., Giovanni, L.D., Massari, R.: Robust fuzzy clustering of multivariate time trajectories. Int. J. Approx. Reason. 99, 12–38 (2018). http://www.sciencedirect.com/science/article/pii/S0888613X17306977
Ferreira, A.M.S., de Oliveira Fontes, C.H., Cavalcante, C.A.M.T., Marambio, J.E.S.: Pattern recognition as a tool to support decision making in the management of the electric sector. Part II: A new method based on clustering of multivariate time series. Int. J. Electr. Power Energy Syst. 67, 613–626 (2015). http://www.sciencedirect.com/science/article/pii/S0142061514007285
Fontes, C.H., Budman, H.: A hybrid clustering approach for multivariate time series – a case study applied to failure analysis in a gas turbine. ISA Transact. 71, 513–529 (2017). http://www.sciencedirect.com/science/article/pii/S0019057817305530
Hu, M., Feng, X., Ji, Z., Yan, K., Zhou, S.: A novel computational approach for discord search with local recurrence rates in multivariate time series. Inform. Sci. 477, 220–233 (2019). http://www.sciencedirect.com/science/article/pii/S0020025516320849
Lee, Y., Na, J., Lee, W.B.: Robust design of ambient-air vaporizer based on time-series clustering. Comput. Chem. Eng. 118, 236–247 (2018). http://www.sciencedirect.com/science/article/pii/S0098135418308822
Li, J., Pedrycz, W., Jamal, I.: Multivariate time series anomaly detection: a framework of hidden Markov models. Appl. Soft Comput. 60, 229–240 (2017). http://www.sciencedirect.com/science/article/pii/S1568494617303782
Liu, G., Zhu, L., Wu, X., Wang, J.: Time series clustering and physical implication for photovoltaic array systems with unknown working conditions. Sol. Energy 180, 401–411 (2019). http://www.sciencedirect.com/science/article/pii/S0038092X19300532
Øyvind Mikalsen, K., Bianchi, F.M., Soguero-Ruiz, C., Jenssen, R.: Time series cluster kernel for learning similarities between multivariate time series with missing data. Pattern Recogn. 76, 569–581 (2018). http://www.sciencedirect.com/science/article/pii/S0031320317304843
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010). https://doi.org/10.1109/TKDE.2009.191
Quast, B.: Recurrent neural networks in r February 2019. https://github.com/bquast/rnn
Salvo, R.D., Montalto, P., Nunnari, G., Neri, M., Puglisi, G.: Multivariate time series clustering on geophysical data recorded at Mt. Etna from 1996 to 2003. J. Volcanol. Geoth. Res. 251, 65–74 (2013). http://www.sciencedirect.com/science/article/pii/S0377027312000443, flank instability at Mt. Etna
Yu, C., Luo, L., Chan, L.L.H., Rakthanmanon, T., Nutanong, S.: A fast LSH-based similarity search method for multivariate time series. Inf. Sci. 476, 337–356 (2019). http://www.sciencedirect.com/science/article/pii/S0020025518308430
Acknowledgment
This research has been funded by the Spanish Ministry of Science and Innovation, under project MINECO-TIN2017-84804-R.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Váquez, I., Villar, J.R., Sedano, J., Simić, S. (2020). A Preliminary Study on Multivariate Time Series Clustering. In: Martínez Álvarez, F., Troncoso Lora, A., Sáez Muñoz, J., Quintián, H., Corchado, E. (eds) 14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019). SOCO 2019. Advances in Intelligent Systems and Computing, vol 950. Springer, Cham. https://doi.org/10.1007/978-3-030-20055-8_45
Download citation
DOI: https://doi.org/10.1007/978-3-030-20055-8_45
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-20054-1
Online ISBN: 978-3-030-20055-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)