1 citations · 2 across the 13 of their papers we have counts for
9 papers · 1 filter
ECHO: Correcting Time Series Foundation Model Forecasts with Historical Analogues and Errors
Boya Zhang, Xilin Dai, Yilin Wang +4
Time Series Foundation Models (TSFMs) provide general-purpose forecasting capabilities, yet their forecasts can remain misaligned with the temporal characteristics of a target doma…
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…
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…
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…
From Samples to Scenarios: A New Paradigm for Probabilistic Forecasting
Xilin Dai, Zhijian Xu, Wanxu Cai +1
Most state-of-the-art probabilistic time series forecasting models rely on sampling to represent future uncertainty. However, this paradigm suffers from inherent limitations, such…