arXiv Machine Learning July 21, 2026 By Andres Potapczynski, Ravi Kiran Selvam, Tatiana Konstantinova, Malcolm Wolff, Kin G. Olivares, Ruijun Ma, Michael W. Mahoney, Andrew Gordon Wilson, Boris N. Oreshkin, Dmitry Efimov

Time-Aware Prior Fitted Networks for Zero-Shot Forecasting with Exogenous Variables

Read the original on arXiv Machine Learning →

arXiv:2603. 15802v2 Announce Type: replace Abstract: In many time series forecasting settings, the target time series is accompanied by exogenous covariates, such as promotions and prices in retail demand; temperature in energy load; calendar and holiday indicators for traffic or sales; and grid load or fuel costs in electricity pricing.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

benchmarks

arXiv AI

Jun 29

The Simulacrum: Decision-Theoretic Pretraining for Near-Optimal Time-Series Forecasting and Inference

arXiv:2606. 27711v1 Announce Type: cross Abstract: We introduce a neural network-based framework for learning time series estimators through a process we term decision-theoretic pretraining.

By Pablo Montero-Manso, Marcel Scharth

benchmarks safety

arXiv Machine Learning

Jun 8

Trio: Learning Time-Series Forecasting with Temporal-Spatial-Sample Attention and Structural Causal Priors

arXiv:2606. 07291v1 Announce Type: new Abstract: Multivariate time-series forecasting requires models to reason over temporal dynamics, cross-variable dependencies, and historical input-output correspondences.

By Tao Chen, Yexu Zhou, Zhi Gong, Hengwei He, Hongda Li, Zhewei Chen, Dongjing Wang, Xin Zhang, Decheng Liu, Chunlei Peng, Zheng Chen, Wenyue Ding

benchmarks

arXiv Machine Learning

Jun 3

FinStressTS: A Parametric Synthetic Benchmark for Time-Series Forecasting in Finance

arXiv:2606. 03184v1 Announce Type: cross Abstract: Financial forecasting is difficult due to low signal-to-noise ratios, latent factors, heavy tails, regime shifts, and jumps.

By Jiaze Sun, Kelvin J. L. Koa, Ruiyang Ni, Yize Liu, Haonan Chen, Ke-Wei Huang

llms multimodal benchmarks safety

arXiv Machine Learning

Jul 7

Evaluating Time Series Foundation Models for Electricity Price Forecasting: Contamination Risk, Distributional Shifts, and Covariate Dependence

arXiv:2607. 02623v1 Announce Type: new Abstract: Time series foundation models (TSFMs) have shown strong zero-shot forecasting performance, but their generalization in covariate-driven, non-stationary settings is underexplored.

By Zhenghua Pan, Ahmed Aziz Ezzat

benchmarks

arXiv Machine Learning

Jul 8

Exogenous Dropout: A Simple, Strong Baseline for Corruption-Robust Time Series Forecasting with Covariates

arXiv:2607. 05452v1 Announce Type: new Abstract: Time series forecasters that use exogenous covariates are fragile in deployment: when those covariates are noised, temporally misaligned, or missing, strong exogenous-fusion and exogenous-adapted models can degrade far above the endogenous-only floor.

By Hao Hu, Xue-shan Ai

benchmarks

arXiv Machine Learning

Jun 5

REGEN: Reference-Guided Synthetic Multivariate Time Series Generation for Forecasting

arXiv:2606. 05264v1 Announce Type: new Abstract: Training robust multivariate time series forecasting models requires large, diverse corpora, yet many real-world domains provide only a handful of observed sequences.

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The Simulacrum: Decision-Theoretic Pretraining for Near-Optimal Time-Series Forecasting and Inference

Trio: Learning Time-Series Forecasting with Temporal-Spatial-Sample Attention and Structural Causal Priors

FinStressTS: A Parametric Synthetic Benchmark for Time-Series Forecasting in Finance

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REGEN: Reference-Guided Synthetic Multivariate Time Series Generation for Forecasting