How Different from the Past? Spatio-Temporal Time Series Forecasting with Self-Supervised Deviation Learning

Haotian Gao, Zheng Dong, Jiawei Yong, Shintaro Fukushima, Kenjiro Taura, Renhe Jiang

Keywords: Spatio-Temporal Forecasting; Deviation Learning;

TL;DR: We propose ST-SSDL, a spatio-temporal forecasting framework by modeling dynamic deviations with self-supervised prototype-based latent structuring.

Abstract: Spatio-temporal forecasting is essential for real-world applications such as traffic management and urban computing. Although recent methods have shown improved accuracy, they often fail to account for dynamic deviations between current inputs and historical patterns. These deviations contain critical signals that can significantly affect model performance. To fill this gap, we propose $\textbf{ST-SSDL}$, a $\underline{S}$patio-$\underline{T}$emporal time series forecasting framework that incorporates a $\underline{S}$elf-$\underline{S}$upervised $\underline{D}$eviation $\underline{L}$earning scheme to capture and utilize such deviations. ST-SSDL anchors each input to its historical average and discretizes the latent space using learnable prototypes that represent typical spatio-temporal patterns. Two auxiliary objectives are proposed to refine this structure: a contrastive loss that enhances inter-prototype discriminability and a deviation loss that regularizes the distance consistency between input representations and corresponding prototypes to quantify deviation. Optimized jointly with the forecasting objective, these components guide the model to organize its hidden space and improve generalization across diverse input conditions. Experiments on six benchmark datasets show that ST-SSDL consistently outperforms state-of-the-art baselines across multiple metrics. Visualizations further demonstrate its ability to adaptively respond to varying levels of deviation in complex spatio-temporal scenarios. Our code and datasets are available at https://github.com/Jimmy-7664/ST-SSDL.

Primary Area: Deep learning (e.g., architectures, generative models, optimization for deep networks, foundation models, LLMs)

Submission Number: 8952