一种通用的ReLearner：通过重新学习输入-标签残差来增强时空预测
A General ReLearner: Empowering Spatiotemporal Prediction by Re-learning Input-label Residual
AI总结：
ReLearner通过引入双向学习机制，有效解决时空预测中输入与标签不一致的问题，提升预测性能。
摘要：
现有的时空预测模型通常采用单向（单向）学习范式，在这种范式下，模型从历史观测输入中提取时空特征，并将它们映射到目标时空空间以进行未来预测（标签）。然而，这些模型在输入和标签之间存在时空差异时往往表现不佳，例如，当具有相似时间序列输入的节点表现出不同的未来标签，或反之亦然时。为了解决这一限制，我们提出在训练阶段显式地纳入标签特征。具体来说，我们引入了时空残差定理，该定理将传统的单向时空预测范式推广为双向学习框架。在此理论基础上，我们设计了一个通用模块，称为ReLearner，它通过辅助的逆向学习过程无缝地将双向学习能力添加到时空神经网络（STNNs）中。在这个过程中，模型重新学习输入数据和未来数据之间的时空特征残差。所提出的ReLearner包含两个关键组成部分：（1）残差学习模块，旨在有效解耦输入和标签表示之间的时空特征差异；（2）残差平滑模块，用于平滑残差项以促进稳定的收敛。在11个真实世界数据集上进行的广泛实验表明，ReLearner显著提高了现有STNNs的预测性能。我们的代码可在GitHub上获得。
英文摘要：
Prevailing spatiotemporal prediction models typically operate under a forward (unidirectional) learning paradigm, in which models extract spatiotemporal features from historical observation input and map them to target spatiotemporal space for future forecasting (label). However, these models frequently exhibit suboptimal performance when spatiotemporal discrepancies exist between inputs and labels, for instance, when nodes with similar time-series inputs manifest distinct future labels, or vice versa. To address this limitation, we propose explicitly incorporating label features during the training phase. Specifically, we introduce the Spatiotemporal Residual Theorem, which generalizes the conventional unidirectional spatiotemporal prediction paradigm into a bidirectional learning framework. Building upon this theoretical foundation, we design an universal module, termed ReLearner, which seamlessly augments Spatiotemporal Neural Networks (STNNs) with a bidirectional learning capability via an auxiliary inverse learning process. In this process, the model relearns the spatiotemporal feature residuals between input data and future data. The proposed ReLearner comprises two critical components: (1) a Residual Learning Module, designed to effectively disentangle spatiotemporal feature discrepancies between input and label representations; and (2) a Residual Smoothing Module, employed to smooth residual terms and facilitate stable convergence. Extensive experiments conducted on 11 real-world datasets across 14 backbone models demonstrate that ReLearner significantly enhances the predictive performance of existing STNNs.Our code is available on GitHub.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
