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}
}