3 citations · 3 across the 7 of their papers we have counts for
4 papers · 1 filter
ViTs: Teaching Machines to See Time Series Anomalies Like Human Experts
Zexin Wang, Changhua Pei, Yang Liu +8
Web service administrators must ensure the stability of multiple systems by promptly detecting anomalies in Key Performance Indicators (KPIs). Achieving the goal of "train once, in…
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…
TimeSeriesBench: An Industrial-Grade Benchmark for Time Series Anomaly Detection Models
Haotian Si, Jianhui Li, Changhua Pei +9
Time series anomaly detection (TSAD) has gained significant attention due to its real-world applications to improve the stability of modern software systems. However, there is no e…
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…