no code implementations • 26 Jan 2024 • Chen Liu, Shibo He, Qihang Zhou, Shizhong Li, Wenchao Meng
To overcome the limitation, we propose \textbf{AnomalyLLM}, a knowledge distillation-based time series anomaly detection approach where the student network is trained to mimic the features of the large language model (LLM)-based teacher network that is pretrained on large-scale datasets.
1 code implementation • 17 Dec 2023 • Qihang Zhou, Shibo He, Haoyu Liu, Jiming Chen, Wenchao Meng
In this paper, we propose MTGFlow, an unsupervised anomaly detection approach for MTS anomaly detection via dynamic Graph and entity-aware normalizing Flow.
3 code implementations • 29 Oct 2023 • Qihang Zhou, Guansong Pang, Yu Tian, Shibo He, Jiming Chen
It is a crucial task when training data is not accessible due to various concerns, eg, data privacy, yet it is challenging since the models need to generalize to anomalies across different domains where the appearance of foreground objects, abnormal regions, and background features, such as defects/tumors on different products/organs, can vary significantly.
2 code implementations • 3 Aug 2022 • Qihang Zhou, Jiming Chen, Haoyu Liu, Shibo He, Wenchao Meng
Multivariate time series anomaly detection has been extensively studied under the semi-supervised setting, where a training dataset with all normal instances is required.