arXiv Machine Learning June 18, 2026 By Yingshuo Wang, Xian Sun, Lingdong Kong, Wei Gao, Yanhang Li, Zhichao Fan, Zexin Zhuang

Do Time Series Foundation Model Benchmarks Hide Regime-Dependent Failures? Evidence from Traffic Speed Forecasting

Read the original on arXiv Machine Learning →

arXiv:2606. 18367v1 Announce Type: new Abstract: Standard benchmarks evaluate time series foundation models (TSFMs) using aggregate metrics, but these can mask severe failures in critical operating regimes.

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

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Asymmetric Peak-Aware Loss for Peak-Critical Time Series Forecasting

Unveiling Stochasticity: Universal Multi-modal Probabilistic Modeling for Traffic Forecasting

CAMP: A Cycle-Aware Multi-Scale Patch Mixer for Time Series Forecasting

Enhancing Irregular Time Series Forecasting with Continuous-Time Modeling Framework

Proxy Reconstruction Pre-training for Ramp Flow Prediction at Highway Interchanges

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