12 papers
When Agent Automation Becomes Profitable: Quantifying and Insuring Autonomous AI Risk through Trace-Economic Underwriting
Binyan Xu, Xilin Dai, Fan Yang +1
AI agents can now take irreversible actions in operational systems, but agent-caused losses are still not clearly assigned, priced, or transferred. Providers often disclaim consequ…
Learning the Context of Errors: Black-Box Online Adaptation of Time Series Foundation Models
Xilin Dai, Yiding Liu, Hongjie Xia +4
The rapid evolution of Time Series Foundation Models (TSFMs) has advanced zero-shot forecasting across diverse domains. Inspired by the current form of Large Language Models, futur…
Navigating the Safety-Fidelity Trade-off: Massive-Variate Time Series Forecasting for Power Systems via Probabilistic Scenarios
Kaijie Xu, Anqi Wang, Xilin Dai
Probabilistic forecasting models are increasingly deployed on multivariate systems with distinct channel physics and operational constraints, but existing benchmarks evaluate neith…
Falcon-X: A Time Series Foundation Model for Heterogeneous Multivariate Modeling
Yiding Liu, Yifan Hu, Hongjie Xia +5
Time series foundation models (TSFMs) are transforming the forecasting paradigm through large-scale cross-domain pretraining. However, most existing TSFMs remain univariate, and re…
Position: Universal Time Series Foundation Models Rest on a Category Error
Xilin Dai, Wanxu Cai, Zhijian Xu +1
This position paper argues that the pursuit of "Universal Foundation Models for Time Series" rests on a fundamental category error, mistaking a structural Container for a semantic…
Fidel-TS: A High-Fidelity Multimodal Benchmark for Time Series Forecasting
Zhijian Xu, Wanxu Cai, Xilin Dai +2
The evaluation of time series forecasting models is hindered by a lack of high-quality benchmarks, leading to overestimated assessments of progress. Existing datasets suffer from i…