Learning with Coherence Patterns in Multivariate Time-series Data via Dynamic Mode Decomposition

Takehito Bito, Masashi Hiraoka, Yoshinobu Kawahara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Understanding complex dynamics in the real world is a fundamental problem in various engineering and scientific fields. Dynamic mode decomposition (DMD) has attracted attention recently as a prominent way to obtain global modal descriptions of nonlinear dynamical processes from data without requiring explicit prior knowledge about the underlying systems. In this paper, we propose a novel learning method for multivariate time-series data involving complex dynamics using coherence patterns among attributes extracted by DMD. To this end, we develop kernels defined with Grassmann subspaces spanned by dynamic modes which are calculated by DMD and represent coherence patters among attributes with respect to the estimated modal dynamics. To incorporate information in labels attached to a set of time-series sequences, we employ a supervised embedding step in the DMD procedure. We illustrate and investigate the empirical performance of the proposed method using real-world data.

Original languageEnglish
Title of host publication2019 International Joint Conference on Neural Networks, IJCNN 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728119854
DOIs
Publication statusPublished - Jul 2019
Event2019 International Joint Conference on Neural Networks, IJCNN 2019 - Budapest, Hungary
Duration: Jul 14 2019Jul 19 2019

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2019-July

Conference

Conference2019 International Joint Conference on Neural Networks, IJCNN 2019
CountryHungary
CityBudapest
Period7/14/197/19/19

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All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

Cite this

Bito, T., Hiraoka, M., & Kawahara, Y. (2019). Learning with Coherence Patterns in Multivariate Time-series Data via Dynamic Mode Decomposition. In 2019 International Joint Conference on Neural Networks, IJCNN 2019 [8852177] (Proceedings of the International Joint Conference on Neural Networks; Vol. 2019-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2019.8852177