Cover Image

Simple and Robust Forecasting of Spatiotemporally Correlated Small Earth Data with A Tabular Foundation Model

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Yang, Yuting, Mei, Gang, Ma, Zhengjing, Xu, Nengxiong, Peng, Jianbing
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Accurate and Robust Generative Approach for Overcoming Data Sparsity and Imbalance in Landslide Modeling with A Tabular Foundation Model
by: Shao, Kaixuan, et al.
Published: (2026)

Statistically Accurate and Robust Generative Prediction of Rock Discontinuities with A Tabular Foundation Model
by: Meng, Han, et al.
Published: (2025)

Tracking the Spatiotemporal Evolution of Landslide Scars Using a Vision Foundation Model: A Novel and Universal Framework
by: Zhou, Meijun, et al.
Published: (2025)

Knowledge-Data Dually Driven Paradigm for Accurate Landslide Susceptibility Prediction under Data-Scarce Conditions Using Geomorphic Priors and Tabular Foundation Model
by: Yang, Yuting, et al.
Published: (2026)

Rainfall-induced Mass Movement as Self-organization Process
by: Ma, Zhengjing, et al.
Published: (2025)

CTSyn: A Foundation Model for Cross Tabular Data Generation
by: Lin, Xiaofeng, et al.
Published: (2024)

Robust Tabular Foundation Models
by: Peroni, Matthew, et al.
Published: (2025)

Causal Data Augmentation for Robust Fine-Tuning of Tabular Foundation Models
by: Bühler, Magnus, et al.
Published: (2026)

Contextualizing MLP-Mixers Spatiotemporally for Urban Data Forecast at Scale
by: Nie, Tong, et al.
Published: (2023)

TabSTAR: A Tabular Foundation Model for Tabular Data with Text Fields
by: Arazi, Alan, et al.
Published: (2025)

Towards Benchmarking Foundation Models for Tabular Data With Text
by: Mráz, Martin, et al.
Published: (2025)

TabularFM: An Open Framework For Tabular Foundational Models
by: Tran, Quan M., et al.
Published: (2024)

On the Robustness of Tabular Foundation Models: Test-Time Attacks and In-Context Defenses
by: Djilani, Mohamed, et al.
Published: (2025)

SPADE-S: A Sparsity-Robust Foundational Forecaster
by: Wolff, Malcolm, et al.
Published: (2025)

TabDPT: Scaling Tabular Foundation Models on Real Data
by: Ma, Junwei, et al.
Published: (2024)

On Finetuning Tabular Foundation Models
by: Rubachev, Ivan, et al.
Published: (2025)

Turning Tabular Foundation Models into Graph Foundation Models
by: Eremeev, Dmitry, et al.
Published: (2025)

Federated Dynamic Modeling and Learning for Spatiotemporal Data Forecasting
by: Pham, Thien, et al.
Published: (2025)

Tabular Foundation Model for Generative Modelling
by: Jiang, Xiangjian, et al.
Published: (2026)

Foundation Model for Lossy Compression of Spatiotemporal Scientific Data
by: Li, Xiao, et al.
Published: (2024)

Distilling Tabular Foundation Models for Structured Health Data
by: Tanna, Aditya, et al.
Published: (2026)

A Mechanistic Study of Tabular Foundation Models
by: Biloš, Marin, et al.
Published: (2026)

Density-Guided Robust Counterfactual Explanations on Tabular Data under Model Multiplicity
by: Tan, Jun, et al.
Published: (2026)

In-context Learning of Evolving Data Streams with Tabular Foundational Models
by: Lourenço, Afonso, et al.
Published: (2025)

A Mamba Foundation Model for Time Series Forecasting
by: Ma, Haoyu, et al.
Published: (2024)

Error Adjustment Based on Spatiotemporal Correlation Fusion for Traffic Forecasting
by: Liu, Fuqiang, et al.
Published: (2025)

Large Language Models Engineer Too Many Simple Features For Tabular Data
by: Küken, Jaris, et al.
Published: (2024)

Can Graphs Improve Tabular Foundation Models?
by: Le, Franck, et al.
Published: (2025)

Real-Time Explanations for Tabular Foundation Models
by: Sena, Luan Borges Teodoro Reis, et al.
Published: (2026)

Active In-Context Learning for Tabular Foundation Models
by: Treerath, Wilailuck, et al.
Published: (2026)

Exploring Fine-Tuning for Tabular Foundation Models
by: Tanna, Aditya, et al.
Published: (2026)

End-to-End Compression for Tabular Foundation Models
by: Zabërgja, Guri, et al.
Published: (2026)

On the Uncertainty Quantification Ability of Tabular Foundation Models
by: Johnson, Tyler R., et al.
Published: (2026)

TabQL: In-Context Q-Learning with Tabular Foundation Models
by: Liu, Qisai, et al.
Published: (2026)

TabICL: A Tabular Foundation Model for In-Context Learning on Large Data
by: Qu, Jingang, et al.
Published: (2025)

TabKDE: Simple and Scalable Tabular Data Generation with Kernel Density Estimates
by: Alishahi, Meysam, et al.
Published: (2026)

TFMLinker: Universal Link Predictor by Graph In-Context Learning with Tabular Foundation Models
by: Liao, Tianyin, et al.
Published: (2026)

Risk In Context: Benchmarking Privacy Leakage of Foundation Models in Synthetic Tabular Data Generation
by: Byun, Jessup, et al.
Published: (2025)

Vision-LLMs for Spatiotemporal Traffic Forecasting
by: Yang, Ning, et al.
Published: (2025)

Towards Fair In-Context Learning with Tabular Foundation Models
by: Kenfack, Patrik, et al.
Published: (2025)