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cs.LG2026
Strong Linear Baselines Strike Back: Closed-Form Linear Models as Gaussian Process Conditional Density Estimators for TSAD
Aleksandr Yugay, Hang Cui, Changhua Pei +1
Research in time series anomaly detection (TSAD) has largely focused on developing increasingly sophisticated, hard-to-train, and expensive-to-infer neural architectures. We revisi…
cs.LG2025
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…
cs.LG2024
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…