TY - GEN
T1 - An investigation into time-varying characteristics of multivariate time series in Grassmann classification
AU - Nuriye, Bezawit Habtamu
AU - Oh, Beomseok
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Since multivariate time series (MTS), which lie in a non-Euclidean space, exhibit temporal evolution and correlation characteristics, its classification is considered a non-trivial task. To mitigate the impact of the time-varying characteristics and thus enhance the classification accuracy, in this paper, we propose to model MTS data using a time-varying linear dynamical system followed by a neural network-based classification on the Grassmannian manifold. Our experiments on publicly available MTS datasets show promising classification results.
AB - Since multivariate time series (MTS), which lie in a non-Euclidean space, exhibit temporal evolution and correlation characteristics, its classification is considered a non-trivial task. To mitigate the impact of the time-varying characteristics and thus enhance the classification accuracy, in this paper, we propose to model MTS data using a time-varying linear dynamical system followed by a neural network-based classification on the Grassmannian manifold. Our experiments on publicly available MTS datasets show promising classification results.
KW - Grassmannian manifold
KW - Linear Dynamical System
KW - Multivariate Time Series
UR - http://www.scopus.com/inward/record.url?scp=85150464027&partnerID=8YFLogxK
U2 - 10.1109/ICEIC57457.2023.10049926
DO - 10.1109/ICEIC57457.2023.10049926
M3 - Conference contribution
AN - SCOPUS:85150464027
T3 - 2023 International Conference on Electronics, Information, and Communication, ICEIC 2023
BT - 2023 International Conference on Electronics, Information, and Communication, ICEIC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Conference on Electronics, Information, and Communication, ICEIC 2023
Y2 - 5 February 2023 through 8 February 2023
ER -