用表格基础模型预测小尺度地球数据，无需任务微调且更准确。
Simple and Robust Forecasting of Spatiotemporally Correlated Small Earth Data with A Tabular Foundation Model
- 基于表格基础模型，捕捉时空模式实现跨场景预测。
- 在三种典型场景中，精度优于T-GCN和TabPFN多数情况。
- 适合缺乏标注数据的地理科学短期预测任务。
小尺度地球数据是具有有限短期监测变化的地球科学观测，测量稀疏但有意义，通常呈现时空相关性。对这类数据进行时空预测对于理解地球科学过程至关重要，尽管其规模较小。然而，传统的时空预测深度学习模型需为不同场景进行特定任务训练。基础模型无需任务微调，但常存在对预训练分布全局均值的预测偏差。本文提出一种简单且鲁棒的时空相关小地球数据预测方法。核心思想是刻画并量化小地球数据的时空模式，进而利用表格基础模型实现跨场景精准预测。在三个典型场景下的对比实验表明，该方法在多数情况下精度优于图神经网络模型（T-GCN）和表格基础模型（TabPFN），表现出更强的鲁棒性。
原文摘要 · Abstract (English)
Small Earth data are geoscience observations with limited short-term monitoring variability, providing sparse but meaningful measurements, typically exhibiting spatiotemporal correlations. Spatiotemporal forecasting on such data is crucial for understanding geoscientific processes despite their small scale. However, conventional deep learning models for spatiotemporal forecasting requires task-specific training for different scenarios. Foundation models do not need task-specific training, but they often exhibit forecasting bias toward the global mean of the pretraining distribution. Here we propose a simple and robust approach for spatiotemporally correlated small Earth data forecasting. The essential idea is to characterize and quantify spatiotemporal patterns of small Earth data and then utilize tabular foundation models for accurate forecasting across different scenarios. Comparative results across three typical scenarios demonstrate that our forecasting approach achieves superior accuracy compared to the graph deep learning model (T-GCN) and tabular foundation model (TabPFN) in the majority of instances, exhibiting stronger robustness.
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