AI RESEARCH
It's TIME: Towards the Next Generation of Time Series Forecasting Benchmarks
arXiv CS.LG
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ArXi:2602.12147v4 Announce Type: replace Time series foundation models (TSFMs) are revolutionizing the forecasting landscape from specific dataset modeling to generalizable task evaluation. However, we contend that existing benchmarks exhibit common limitations in four dimensions: constrained data composition dominated by reused legacy sources, compromised data integrity lacking rigorous quality assurance, misaligned task formulations detached from real-world contexts, and rigid analysis perspectives that obscure generalizable insights. To bridge these gaps, we