通过分辨分辨率动态检索数据，提升无历史数据场景下的气候预测精度。
Resolution-Aware Retrieval Augmented Zero-Shot Forecasting
- 按频率分解信号，低频用广域上下文，高频用局部信息。
- 在ERA5数据集上比HRRR低71%的均方误差，比Chronos低34%。
- 适合缺乏历史数据的微气候预测，尤其适合资源受限场景。
零样本预测旨在无需直接历史数据的情况下预测未见条件下的结果，这对传统预测方法构成重大挑战。我们提出一种分辨率感知的检索增强预测模型，通过利用空间相关性和时间频率特征提升预测精度。该模型将信号分解为不同频率成分：低频成分依赖更广的空间上下文，高频成分聚焦局部影响。这使得模型能动态检索相关数据，并在极少历史数据条件下适应新位置。应用于微气候预测时，该模型显著优于传统方法、数值天气预报模型及现代基础时间序列模型，在ERA5数据集上相比HRRR降低71%的均方误差，相比Chronos降低34%。结果表明，检索增强与分辨率感知策略有效，为微气候建模及其他领域的零样本预测提供了可扩展且数据高效的新方案。
原文摘要 · Abstract (English)
Zero-shot forecasting aims to predict outcomes for previously unseen conditions without direct historical data, posing a significant challenge for traditional forecasting methods. We introduce a Resolution-Aware Retrieval-Augmented Forecasting model that enhances predictive accuracy by leveraging spatial correlations and temporal frequency characteristics. By decomposing signals into different frequency components, our model employs resolution-aware retrieval, where lower-frequency components rely on broader spatial context, while higher-frequency components focus on local influences. This allows the model to dynamically retrieve relevant data and adapt to new locations with minimal historical context. Applied to microclimate forecasting, our model significantly outperforms traditional forecasting methods, numerical weather prediction models, and modern foundation time series models, achieving 71% lower MSE than HRRR and 34% lower MSE than Chronos on the ERA5 dataset. Our results highlight the effectiveness of retrieval-augmented and resolution-aware strategies, offering a scalable and data-efficient solution for zero-shot forecasting in microclimate modeling and beyond.
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