6 papers · 1 filter
A Unified Scaling Law for Time Series Foundation Models
Xilin Dai, Yiding Liu, Zewei Dong +2
We develop a Unified Scaling Law and a Unified Theory of Time Series Learning to understand how model capacity and historical information support forecasting. Across different look…
Pythia: Toward Foundation World Models for Multimodal Time Series
Xilin Dai, Hongzhou Chen, Yifan Hu +3
Time-series foundation models offer a unified approach to forecasting across heterogeneous domains. Textual context and auxiliary observations provide complementary information abo…
When Tomorrow Becomes Today: Self-Evolving Policies for Agentic Time-Series Forecasting
Yifan Hu, Xilin Dai, Zhiyuan Qu +4
Agentic time series forecasting concerns systems whose underlying mechanisms evolve, making the relative effectiveness of numerical models, reasoning strategies, and intervention r…
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…
Existence Precedes Value: Joint Modeling of Observational Existence and Evolving States in Time Series Forecasting
Yifan Hu, Hongzhou Chen, Peiyuan Liu +3
Real-world time series are often highly incomplete and irregular due to sensor dormancy, transmission delays, and event-driven sampling, making reliable forecasting fundamentally c…
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…