AI 中文总结
研究针对现有空气质量预测基准不足，提出空气质量竞技场（AQA），它是含多国多污染物的数据集及基准。通过在11个模型和基线测试，表明时间序列基础模型是有效零样本预测器，表现最佳的模型采用跨模态架构，AQA已公开发布。
AI 中文摘要
空气污染每年导致约790万人过早死亡，因此准确预测成为关键的公共卫生优先事项。机器学习越来越多地用于预测空气污染水平，但现有的基准在地理范围和污染物覆盖方面都很有限，且未能在真实世界的大规模数据上评估最新一代的时间序列基础模型（TSFMs）。我们提出了空气质量竞技场（AQA），一个大规模的多国多污染物数据集（AQA-Data）和基准（AQA-Bench）来弥补这一差距。AQA涵盖7个不同国家和4个大陆三年期间的6种主要污染物，有超过14000个站点-污染物序列，旨在为空气质量任务提供全面基准。我们在11个领先的时间序列基础模型和经典基线对该数据集进行基准测试，以评估短期空气质量预测的性能。结果表明TSFMs是有效的零样本预测器，始终优于经典基线，表现最佳的模型采用了利用视觉基础模型进行时间序列预测的跨模态架构。AQA已在该http网址公开发布。
英文摘要
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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