学习时间感知图结构用于空间相关时间序列预测
Learning Time-aware Graph Structures for Spatially Correlated Time Series Forecasting
AI总结：
针对现有图方法无法建模时空数据中时变相关性的问题，提出时间感知图结构学习（TagSL）与图卷积门控循环单元（GCGRU）结合的TGCRN框架，在五个真实数据集上实现最先进预测性能。
摘要：
空间相关时间序列的未来值时空预测在许多网络物理系统（CPS）中非常重要。近期研究证明，使用图神经网络捕获时间序列之间的潜在相关性具有增强预测的潜力。然而，大多数现有方法依赖于预定义或自学习的图，这些图要么是静态的，要么是无意中动态的，因此无法建模由CPS中底层过程规律性引起的、呈现趋势和周期性的时变相关性。为解决这一局限，我们提出了时间感知图结构学习（TagSL），通过在高维空间中测量节点和时间表示的交互来提取时间序列之间的时间感知相关性。值得注意的是，我们引入了时间差异学习，利用带有基于距离的正则化项的对比学习，将学习到的空间相关性约束为趋势序列。此外，我们提出了一个周期性判别函数，以便从节点状态中捕获周期性变化。接下来，我们提出了一种基于图卷积的门控循环单元（GCGRU），在联合捕获空间和时间依赖性的同时学习时间感知和节点特定的模式。最后，我们引入了一个统一框架，名为时间感知图卷积循环网络（TGCRN），在编码器-解码器架构中结合TagSL和GCGRU，用于多步时空预测。我们在五个真实世界数据集上报告了TGCRN和流行现有方法的实验，从而证明TGCRN能够推进最先进水平。我们还进行了详细的消融研究和可视化分析，提供了对时间感知结构学习有效性的深入见解。
英文摘要：
Spatio-temporal forecasting of future values of spatially correlated time series is important across many cyber-physical systems (CPS). Recent studies offer evidence that the use of graph neural networks to capture latent correlations between time series holds a potential for enhanced forecasting. However, most existing methods rely on pre-defined or self-learning graphs, which are either static or unintentionally dynamic, and thus cannot model the time-varying correlations that exhibit trends and periodicities caused by the regularity of the underlying processes in CPS. To tackle such limitation, we propose Time-aware Graph Structure Learning (TagSL), which extracts time-aware correlations among time series by measuring the interaction of node and time representations in high-dimensional spaces. Notably, we introduce time discrepancy learning that utilizes contrastive learning with distance-based regularization terms to constrain learned spatial correlations to a trend sequence. Additionally, we propose a periodic discriminant function to enable the capture of periodic changes from the state of nodes. Next, we present a Graph Convolution-based Gated Recurrent Unit (GCGRU) that jointly captures spatial and temporal dependencies while learning time-aware and node-specific patterns. Finally, we introduce a unified framework named Time-aware Graph Convolutional Recurrent Network (TGCRN), combining TagSL, and GCGRU in an encoder-decoder architecture for multi-step spatio-temporal forecasting. We report on experiments with TGCRN and popular existing approaches on five real-world datasets, thus providing evidence that TGCRN is capable of advancing the state-of-the-art. We also cover a detailed ablation study and visualization analysis, offering detailed insight into the effectiveness of time-aware structure learning.
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