3 papers
cs.LG2025
Are Time-Series Foundation Models Deployment-Ready? A Systematic Study of Adversarial Robustness Across Domains
Jiawen Zhang, Zhenwei Zhang, Shun Zheng +3
Time-Series Foundation Models (TSFMs) are rapidly transitioning from research prototypes to core components of critical decision-making systems, driven by their impressive zero-sho…
cs.LG2025
Unify and Anchor: A Context-Aware Transformer for Cross-Domain Time Series Forecasting
Xiaobin Hong, Jiawen Zhang, Wenzhong Li +2
The rise of foundation models has revolutionized natural language processing and computer vision, yet their best practices to time series forecasting remains underexplored. Existin…
cs.LG2024
ElasTST: Towards Robust Varied-Horizon Forecasting with Elastic Time-Series Transformer
Jiawen Zhang, Shun Zheng, Xumeng Wen +3
Numerous industrial sectors necessitate models capable of providing robust forecasts across various horizons. Despite the recent strides in crafting specific architectures for time…