构建首个多国多污染物空气污染预测大模型基准，支持时序大模型评估。
Air Quality Arena: A Large-Scale Multi-Region Ground Monitoring Dataset and Benchmark for Air Quality Forecasting with Time-Series Foundation Models
- 构建覆盖7国4洲的多污染物时序数据集，含1.4万+站点-污染物序列。
- 11种时序大模型在该数据集上表现优于传统方法，跨模态模型最优。
- 适合关注空气质量预测与时序大模型应用的研究者使用。
空气污染每年导致约790万例过早死亡，精准预测成为重要公共卫生议题。机器学习在空气污染预测中日益应用，但现有基准在地理范围和污染物覆盖上均有限，且未在真实大规模数据上评估最新时序基础模型（TSFMs）。我们提出空气品质竞技场（AQA），包含大规模多国多污染物数据集（AQA-Data）与基准（AQA-Bench），覆盖3年期间6种主要污染物，涵盖7个不同国家及4大洲，共超过14,000个站点-污染物时间序列，旨在提供全面的空气品质任务评估基准。我们在11种领先的时序基础模型与经典基线模型上对该数据集进行评估，测试其短期预测性能。结果表明，时序基础模型具备有效的零样本预测能力，整体表现显著优于传统基线，其中采用跨模态架构、利用视觉基础模型进行时间序列建模的模型表现最佳。AQA已公开发布于AirQualityArena.github.io。
原文摘要 · Abstract (English)
Air pollution causes an estimated 7.9 million premature deaths annually, making accurate forecasting a critical public health priority. Machine learning is increasingly being applied to forecast air pollution levels, yet existing benchmarks remain narrow in both geographic scope and pollutant coverage, and fail to evaluate the latest generation of time series foundation models (TSFMs) on real world, large scale data. We present Air Quality Arena (AQA), a large scale multi-country and multi-pollutant dataset (AQA-Data) and benchmark (AQA-Bench) to address this gap. AQA covers 6 major pollutants over a three year period across 7 diverse countries and 4 continents, with more than 14,000 station-pollutant series, aiming to provide a comprehensive benchmark for air quality tasks. We benchmark this dataset across 11 leading time series foundation models and classical baselines to assess performance on short-term air quality forecasting. Our results demonstrate that TSFMs are effective zero-shot forecasters and consistently outperform classical baselines, with our top-performing model employing a cross-modal architecture that leverages a vision foundation model for time series forecasting. AQA is publicly released at AirQualityArena.github.io
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