[2602.12147] It's TIME: Towards the Next Generation of Time Series Forecasting Benchmarks

Computer Science > Machine Learning arXiv:2602.12147 (cs) [Submitted on 12 Feb 2026 (v1), last revised 27 Feb 2026 (this version, v2)] Title:It's TIME: Towards the Next Generation of Time Series Forecasting Benchmarks Authors:Zhongzheng Qiao, Sheng Pan, Anni Wang, Viktoriya Zhukova, Yong Liu, Xudong Jiang, Qingsong Wen, Mingsheng Long, Ming Jin, Chenghao Liu View a PDF of the paper titled It's TIME: Towards the Next Generation of Time Series Forecasting Benchmarks, by Zhongzheng Qiao and 9 other authors View PDF HTML (experimental) Abstract: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 introduce TIME, a next-generation task-centric benchmark comprising 50 fresh datasets and 98 forecasting tasks, tailored for strict zero-shot TSFM evaluation free from data leakage. Integrating large language models and human expertise, we establish a rigorous human-in-the-loop benchmark construction pipeline to ensure high data integrity and redefine task formulation by aligning forecasting configurations with real-wor...