Abstract
Big Data and the IoT explosion has made clustering multivariate Time Series (TS) one of the most effervescent research fields. From Bio-informatics to 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. This study represents a step forward in our research. We firstly made use of Recurrent Neural Networks and transfer learning to analyze each example, measuring similarities between variables. All the results are finally aggregated to create an adjacency matrix that allows extracting the groups. In this second approach, splines are introduced to smooth the TS before modeling; also, this step avoid to learn from data with high variation or with noise. In the experiments, the two solutions are compared suing the same proof-of-concept experimentation.
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. Approximate Reasoning 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 Trans. 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. Inf. 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. Solar Energy 180, 401–411 (2019). http://www.sciencedirect.com/science/article/pii/S0038092X19300532
Mikalsen, K.Ø., 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). Flank instability at Mt. Etna. http://www.sciencedirect.com/science/article/pii/S0377027312000443
Váquez, I., Villar, J.R., Sedano, J., Simić, S.: A preliminary study on multivariate time series clustering. In: Martínez Álvarez, F., Troncoso Lora, A., Sáez Muñoz, J.A., Quintián, H., Corchado, E. (eds.) SOCO 2019. AISC, vol. 950, pp. 473–480. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-20055-8_45
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, and by the Grant FC-GRUPIN-IDI/2018/000226 from the Asturias Regional Government.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Vázquez, I., Villar, J.R., Sedano, J., Simić, S., de la Cal, E. (2019). A Proof of Concept in Multivariate Time Series Clustering Using Recurrent Neural Networks and SP-Lines. In: Pérez García, H., Sánchez González, L., Castejón Limas, M., Quintián Pardo, H., Corchado Rodríguez, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2019. Lecture Notes in Computer Science(), vol 11734. Springer, Cham. https://doi.org/10.1007/978-3-030-29859-3_30
Download citation
DOI: https://doi.org/10.1007/978-3-030-29859-3_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29858-6
Online ISBN: 978-3-030-29859-3
eBook Packages: Computer ScienceComputer Science (R0)