Imputation-based Time-Series Anomaly Detection with Conditional Weight-Incremental Diffusion Models-Reference-Cited by-同舟云学术

Imputation-based Time-Series Anomaly Detection with Conditional Weight-Incremental Diffusion Models

Published:2023-08-04 Issue: Volume: Page:
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Container-title:Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Xiao Chunjing¹^ORCID,Gou Zehua¹^ORCID,Tai Wenxin²^ORCID,Zhang Kunpeng³^ORCID,Zhou Fan²^ORCID

Affiliation:

1. Henan University, Kaifeng, China

2. University of Electronic Science and Technology of China, Chengdu, China

3. University of Maryland, College Park, College Park, MD, USA

Funder

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3580305.3599391

Reference60 articles.

1. Ahmed Abdulaal Zhuanghua Liu and Tomer Lancewicki. 2021. Practical approach to asynchronous multivariate time series anomaly detection and localization. In KDD. 2485--2494. Ahmed Abdulaal Zhuanghua Liu and Tomer Lancewicki. 2021. Practical approach to asynchronous multivariate time series anomaly detection and localization. In KDD. 2485--2494.

2. Real-Time Change Point Detection with Application to Smart Home Time Series Data

3. USAD

4. A Review on Outlier/Anomaly Detection in Time Series Data

5. Mohammad Braei and Sebastian Wagner . 2020. Anomaly detection in univariate time-series: A survey on the state-of-the-art. arXiv ( 2020 ). Mohammad Braei and Sebastian Wagner. 2020. Anomaly detection in univariate time-series: A survey on the state-of-the-art. arXiv (2020).

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