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} }