Few-shot time-series anomaly detection with unsupervised domain adaptation-Reference-Cited by-同舟云学术

Few-shot time-series anomaly detection with unsupervised domain adaptation

Published:2023-11 Issue: Volume:649 Page:119610
ISSN:0020-0255
Container-title:Information Sciences
language:en
Short-container-title:Information Sciences

Author:

Li Hongbo^ORCID,Zheng Wenli^ORCID,Tang Feilong,Zhu Yanmin^ORCID,Huang Jielong

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Computer Science Applications,Theoretical Computer Science,Control and Systems Engineering,Software

Reference50 articles.

1. Tensorflow: a system for large-scale machine learning;Abadi,2016

2. Unsupervised anomaly detection approach for time-series in multi-domains using deep reconstruction error;Amarbayasgalan;Symmetry,2020

3. A theory of learning from different domains;Ben-David;Mach. Learn.,2010

4. Model-agnostic meta-learning for fast adaptation of deep networks;Finn,2017

Cited by 10 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. MCGnet with multi-level gated unit transformations: A time series prediction-based model for unknown pattern abnormality detection;Expert Systems with Applications;2024-12

2. Multi-source refined adversarial domain adaptation with transfer complementarity infusion for IoT intrusion detection under limited samples;Expert Systems with Applications;2024-11

3. Optimizing multi-time series forecasting for enhanced cloud resource utilization based on machine learning;Knowledge-Based Systems;2024-11

4. Few-shot online anomaly detection and segmentation;Knowledge-Based Systems;2024-09

5. A robust multi-scale feature extraction framework with dual memory module for multivariate time series anomaly detection;Neural Networks;2024-09