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.

翻译：零样本预测旨在针对未见过的条件预测结果，而无需直接的历史数据，这对传统预测方法构成了重大挑战。我们提出了一种分辨率感知检索增强预测模型，该模型通过利用空间相关性和时间频率特征来提升预测精度。通过将信号分解为不同的频率分量，我们的模型采用分辨率感知检索机制，其中低频分量依赖更广泛的空间上下文，而高频分量则聚焦于局部影响。这使得模型能够动态检索相关数据，并在历史背景极少的情况下适应新地点。应用于微气候预测时，我们的模型显著优于传统预测方法、数值天气预报模型以及现代基础时间序列模型，在ERA5数据集上实现了比HRRR低71%的均方误差，比Chronos低34%的均方误差。我们的结果凸显了检索增强和分辨率感知策略的有效性，为微气候建模及其他领域的零样本预测提供了一种可扩展且数据高效的解决方案。