通过联合频域监督，让模型更准确预测时空依赖关系。
Decorrelating the Future: Joint Frequency Domain Learning for Spatio-temporal Forecasting
- 引入联合频域损失函数，同时优化时空维度的预测
- 在6个真实数据集上提升现有模型性能，最高改善18.3%
- 适合需要精确捕捉时空动态的交通、气象预测任务
标准的直接预测模型通常依赖点对点的目标函数（如均方误差），难以捕捉图结构信号中的复杂时空依赖。尽管近期的频域方法（如FreDF）缓解了时间自相关问题，但常忽略空间及跨时空交互。为此，我们提出FreST Loss，一种增强型频域时空训练目标，将监督扩展至联合时空谱域。通过联合傅里叶变换（JFT），FreST Loss在统一频域中对齐模型预测与真实值，有效解耦时空维度上的复杂依赖。理论分析表明，该形式可降低时域训练目标带来的估计偏差。在六个真实世界数据集上的大量实验表明，FreST Loss具有模型无关性，能持续提升当前最优基线模型表现，更好捕捉整体时空动态。
原文摘要 · Abstract (English)
Standard direct forecasting models typically rely on point-wise objectives such as Mean Squared Error, which fail to capture the complex spatio-temporal dependencies inherent in graph-structured signals. While recent frequency-domain approaches such as FreDF mitigate temporal autocorrelation, they often overlook spatial and cross spatio-temporal interactions. To address this limitation, we propose FreST Loss, a frequency-enhanced spatio-temporal training objective that extends supervision to the joint spatio-temporal spectrum. By leveraging the Joint Fourier Transform (JFT), FreST Loss aligns model predictions with ground truth in a unified spectral domain, effectively decorrelating complex dependencies across both space and time. Theoretical analysis shows that this formulation reduces estimation bias associated with time-domain training objectives. Extensive experiments on six real-world datasets demonstrate that FreST Loss is model-agnostic and consistently improves state-of-the-art baselines by better capturing holistic spatio-temporal dynamics.
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