all AI news
Towards a Rigorous Evaluation of Time-series Anomaly Detection. (arXiv:2109.05257v2 [cs.LG] UPDATED)
Jan. 5, 2022, 2:10 a.m. | Siwon Kim, Kukjin Choi, Hyun-Soo Choi, Byunghan Lee, Sungroh Yoon
cs.LG updates on arXiv.org arxiv.org
In recent years, proposed studies on time-series anomaly detection (TAD)
report high F1 scores on benchmark TAD datasets, giving the impression of clear
improvements in TAD. However, most studies apply a peculiar evaluation protocol
called point adjustment (PA) before scoring. In this paper, we theoretically
and experimentally reveal that the PA protocol has a great possibility of
overestimating the detection performance; that is, even a random anomaly score
can easily turn into a state-of-the-art TAD method. Therefore, the comparison
of …
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Senior ML Researcher - 3D Geometry Processing | 3D Shape Generation | 3D Mesh Data
@ Promaton | Europe
Senior AI Engineer, EdTech (Remote)
@ Lightci | Toronto, Ontario
Data Scientist for Salesforce Applications
@ ManTech | 781G - Customer Site,San Antonio,TX
AI Research Scientist
@ Gridmatic | Cupertino, CA
Data Engineer
@ Global Atlantic Financial Group | Boston, Massachusetts, United States
Machine Learning Engineer - Conversation AI
@ DoorDash | Sunnyvale, CA; San Francisco, CA; Seattle, WA; Los Angeles, CA