提出结构保持对比学习，提升时空序列建模精度
Structure-preserving contrastive learning for spatial time series
- 设计两种正则化项，保留样本间相似性拓扑与图几何结构
- 动态加权机制平衡对比学习与结构保持，训练更稳定
- 适用于交通流预测等带空间特征的时间序列，通用性强
神经网络模型性能高度依赖从数据中学习有意义的潜在模式。自监督表示学习可提升模型表现和泛化能力，但针对具有空间特性的时间序列（如交通领域常见数据），需在潜在空间中保持细粒度的时空相似性，面临独特挑战。本文提出两种结构保持正则化方法：一种保留实例间相似性的拓扑结构，另一种保留时空维度上相似性的图几何结构。为平衡对比学习目标与结构保持需求，设计动态加权机制，自适应调节二者关系，稳定训练过程。通过多变量时间序列分类、宏观与微观交通预测等广泛实验验证，该方法在所有任务中均更有效地保持相似性结构，并提升现有最优模型性能。该方法可嵌入任意神经网络，尤其适合具地理特征的时间序列数据。研究还表明，潜在空间中良好保留的相似性结构，对应更具信息量的表示，为数据驱动的交通研究提供新设计思路。代码与结果数据已公开于 https://github.com/yiru-jiao/spclt。
原文摘要 · Abstract (English)
The effectiveness of neural network models largely relies on learning meaningful latent patterns from data, where self-supervised learning of informative representations can enhance model performance and generalisability. However, self-supervised representation learning for spatially characterised time series, which are ubiquitous in transportation domain, poses unique challenges due to the necessity of maintaining fine-grained spatio-temporal similarities in the latent space. In this study, we introduce two structure-preserving regularisers for the contrastive learning of spatial time series: one regulariser preserves the topology of similarities between instances, and the other preserves the graph geometry of similarities across spatial and temporal dimensions. To balance the contrastive learning objective and the need for structure preservation, we propose a dynamic weighting mechanism that adaptively manages this trade-off and stabilises training. We validate the proposed method through extensive experiments, including multivariate time series classification to demonstrate its general applicability, as well as macroscopic and microscopic traffic prediction to highlight its particular usefulness in encoding traffic interactions. Across all tasks, our method preserves the similarity structures more effectively and improves state-of-the-art task performances. This method can be integrated with an arbitrary neural network model and is particularly beneficial for time series data with spatial or geographical features. Furthermore, our findings suggest that well-preserved similarity structures in the latent space indicate more informative and useful representations. This provides insights to design more effective neural networks for data-driven transportation research. Our code is made openly accessible with all resulting data at https://github.com/yiru-jiao/spclt
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