3 papers
cs.LG2026
Rethinking Weak Supervision in Anomaly Detection: A Comprehensive Benchmark
Xu Yao, Siyuan Zhou, Zhenbo Wu +6
Weakly supervised anomaly detection (WSAD) has developed in three primary directions: incomplete, inexact, and inaccurate supervision. However, these directions remain isolated, la…
cs.LG2026
Beyond Holistic Models: Systematic Component-level Benchmarking of Deep Multivariate Time-Series Forecasting
Shuang Liang, Chaochuan Hou, Xu Yao +4
While previous research in multivariate time series forecasting has focused on developing complex holistic models, this work advocates for a shift toward a granular, component-leve…
cs.LG2025
TSGym: Design Choices for Deep Multivariate Time-Series Forecasting
Shuang Liang, Chaochuan Hou, Xu Yao +4
Recently, deep learning has driven significant advancements in multivariate time series forecasting (MTSF) tasks. However, much of the current research in MTSF tends to evaluate mo…