4 papers
TShape: Rescuing Machine Learning Models from Complex Shapelet Anomalies
Hang Cui, Jingjing Li, Haotian Si +4
Time series anomaly detection (TSAD) is critical for maintaining the reliability of modern IT infrastructures, where complex anomalies frequently arise in highly dynamic environmen…
A Survey on AgentOps: Categorization, Challenges, and Future Directions
Zexin Wang, Jingjing Li, Quan Zhou +7
As the reasoning capabilities of Large Language Models (LLMs) continue to advance, LLM-based agent systems offer advantages in flexibility and interpretability over traditional sys…
CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations
Haotian Si, Changhua Pei, Jianhui Li +2
Recent advances in lightweight time series forecasting models suggest the inherent simplicity of time series forecasting tasks. In this paper, we present CMoS, a super-lightweight…
Beyond Sharing: Conflict-Aware Multivariate Time Series Anomaly Detection
Haotian Si, Changhua Pei, Zhihan Li +7
Massive key performance indicators (KPIs) are monitored as multivariate time series data (MTS) to ensure the reliability of the software applications and service system. Accurately…