E2Usd: Efficient-yet-effective Unsupervised State Detection for Multivariate Time Series.

Zhichen Lai|Huan Li|Dalin Zhang|Yan Zhao|Weizhu Qian|Christian S. Jensen


Anthology ID:DBLP:conf/www/0001L00QJ24
Volume:Proceedings of the ACM on Web Conference 2024, WWW 2024, Singapore, May 13-17, 2024
Year:2024
Venue:The Web Conference (WWW)
Publisher:ACM
Pages:3010-3021
URL:https://doi.org/10.1145/3589334.3645593
DOI:https://doi.org/10.1145/3589334.3645593
DBLP:conf/www/0001L00QJ24
BibTeX:
@inproceedings{lai-2024-e2usd, author = {Zhichen Lai and Huan Li and Dalin Zhang and Yan Zhao and Weizhu Qian and Christian S. Jensen}, editor = {Tat-Seng Chua and Chong-Wah Ngo and Ravi Kumar and Hady Wirawan Lauw and Roy Ka-Wei Lee}, title = {{E2Usd: Efficient-yet-effective Unsupervised State Detection for Multivariate Time Series}}, booktitle = {{Proceedings of the ACM on Web Conference 2024, WWW 2024, Singapore, May 13-17, 2024}}, pages = {3010--3021}, publisher = {ACM}, year = {2024}, url = {https://doi.org/10.1145/3589334.3645593}, doi = {https://doi.org/10.1145/3589334.3645593} }