FluxEV: A Fast and Effective Unsupervised Framework for Time-Series Anomaly Detection.
Jia Li|Shimin Di|Yanyan Shen|Lei Chen
| Anthology ID: | DBLP:conf/wsdm/LiDSC21 |
|---|---|
| Volume: | WSDM '21, The Fourteenth ACM International Conference on Web Search and Data Mining, Virtual Event, Israel, March 8-12, 2021 |
| Year: | 2021 |
| Venue: | Web Search and Data Mining (WSDM) |
| Publisher: | ACM |
| Pages: | 824-832 |
| URL: | https://doi.org/10.1145/3437963.3441823 |
| DOI: | https://doi.org/10.1145/3437963.3441823 |
| DBLP: | conf/wsdm/LiDSC21 |
| BibTeX: |
@inproceedings{li-2021-fluxev,
author = {Jia Li and
Shimin Di and
Yanyan Shen and
Lei Chen},
editor = {Liane Lewin-Eytan and
David Carmel and
Elad Yom-Tov and
Eugene Agichtein and
Evgeniy Gabrilovich},
title = {{FluxEV: A Fast and Effective Unsupervised Framework for Time-Series Anomaly Detection}},
booktitle = {{WSDM '21, The Fourteenth ACM International Conference on Web Search and Data Mining, Virtual Event, Israel, March 8-12, 2021}},
pages = {824--832},
publisher = {ACM},
year = {2021},
url = {https://doi.org/10.1145/3437963.3441823},
doi = {https://doi.org/10.1145/3437963.3441823}
}