87 citations · 126 across the 10 of their papers we have counts for
8 papers · 1 filter
CaPulse: Detecting Anomalies by Tuning in to the Causal Rhythms of Time Series
Yutong Xia, Yingying Zhang, Yuxuan Liang +3
Time series anomaly detection has garnered considerable attention across diverse domains. While existing methods often fail to capture the underlying mechanisms behind anomaly gene…
Abnormality Forecasting: Time Series Anomaly Prediction via Future Context Modeling
Sinong Zhao, Wenrui Wang, Hongzuo Xu +5
Identifying anomalies from time series data plays an important role in various fields such as infrastructure security, intelligent operation and maintenance, and space exploration.…
Cluster-Wide Task Slowdown Detection in Cloud System
Feiyi Chen, Yingying Zhang, Lunting Fan +4
Slow task detection is a critical problem in cloud operation and maintenance since it is highly related to user experience and can bring substantial liquidated damages. Most anomal…
Generative Semi-supervised Graph Anomaly Detection
Hezhe Qiao, Qingsong Wen, Xiaoli Li +2
This work considers a practical semi-supervised graph anomaly detection (GAD) scenario, where part of the nodes in a graph are known to be normal, contrasting to the extensively ex…
Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting
Peng Chen, Yingying Zhang, Yunyao Cheng +5
Transformers for time series forecasting mainly model time series from limited or fixed scales, making it challenging to capture different characteristics spanning various scales.…
Explaining Time Series via Contrastive and Locally Sparse Perturbations
Zichuan Liu, Yingying Zhang, Tianchun Wang +8
Explaining multivariate time series is a compound challenge, as it requires identifying important locations in the time series and matching complex temporal patterns. Although prev…
Learning Multi-Pattern Normalities in the Frequency Domain for Efficient Time Series Anomaly Detection
Feiyi Chen, Yingying zhang, Zhen Qin +5
Anomaly detection significantly enhances the robustness of cloud systems. While neural network-based methods have recently demonstrated strong advantages, they encounter practical…
LARA: A Light and Anti-overfitting Retraining Approach for Unsupervised Time Series Anomaly Detection
Feiyi Chen, Zhen Qin, Yingying Zhang +4
Most of current anomaly detection models assume that the normal pattern remains same all the time. However, the normal patterns of Web services change dramatically and frequently.…