activity
20182026
most citedGluonTS: Probabilistic Time Series Models in Python

77 citations · 144 across the 7 of their papers we have counts for

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Showing cs.LGShow all

21 papers · 1 filter

cs.LG2026

Mitra-v2 Technical Report

Yefan Tao, Xiyuan Zhang, Xinyi Liu +13

We introduce Mitra-v2, a tabular foundation model that delivers state-of-the-art performance on real-world classification and regression problems, from credit-risk scoring and clin…

cs.LG2026

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…

cs.LG20251 cited

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…

cs.LG2025

Understanding the Implicit Biases of Design Choices for Time Series Foundation Models

Annan Yu, Danielle C. Maddix, Boran Han +7

Time series foundation models (TSFMs) are a class of potentially powerful, general-purpose tools for time series forecasting and related temporal tasks, but their behavior is stron…

cs.LG20257 cited

Chronos-2: From Univariate to Universal Forecasting

Abdul Fatir Ansari, Oleksandr Shchur, Jaris Küken +20

Pretrained time series models have enabled inference-only forecasting systems that produce accurate predictions without task-specific training. However, existing approaches largely…

cs.LG2025

Understanding Transformers for Time Series: Rank Structure, Flow-of-ranks, and Compressibility

Annan Yu, Danielle C. Maddix, Boran Han +7

Transformers are widely used across data modalities, and yet the principles distilled from text models often transfer imperfectly to models trained to other modalities. In this pap…