1 citations · 1 across the 6 of their papers we have counts for
7 papers
PathFM: Fast Generative Forecasting of Multivariate Trajectories
Jiachen Yao, Xiyuan Zhang, Annan Yu +5
Time series foundation models are becoming increasingly popular for general-purpose forecasting. However, most leading models predict marginal quantiles, leaving the dependence str…
SenTSR-Bench: Thinking with Injected Knowledge for Time-Series Reasoning
Zelin He, Boran Han, Xiyuan Zhang +10
Time-series diagnostic reasoning is essential for many applications, yet existing solutions face a persistent gap: general reasoning large language models (GRLMs) possess strong re…
Mitra: Mixed Synthetic Priors for Enhancing Tabular Foundation Models
Xiyuan Zhang, Danielle C. Maddix, Junming Yin +11
Since the seminal work of TabPFN, research on tabular foundation models (TFMs) based on in-context learning (ICL) has challenged long-standing paradigms in machine learning. Withou…
Test-Time Efficient Pretrained Model Portfolios for Time Series Forecasting
Mert Kayaalp, Caner Turkmen, Oleksandr Shchur +4
Is bigger always better for time series foundation models? With the question in mind, we explore an alternative to training a single, large monolithic model: building a portfolio o…
Zero-Shot Time Series Forecasting with Covariates via In-Context Learning
Andreas Auer, Raghul Parthipan, Pedro Mercado +5
Pretrained time series models, capable of zero-shot forecasting, have demonstrated significant potential in enhancing both the performance and accessibility of time series forecast…
When Does Multimodality Lead to Better Time Series Forecasting?
Xiyuan Zhang, Boran Han, Haoyang Fang +11
Recently, there has been growing interest in incorporating textual information into foundation models for time series forecasting. However, it remains unclear whether and under wha…
Gradient-Free Generation for Hard-Constrained Systems
Chaoran Cheng, Boran Han, Danielle C. Maddix +4
Generative models that satisfy hard constraints are critical in many scientific and engineering applications, where physical laws or system requirements must be strictly respected.…