ST-ReP: Learning Predictive Representations Efficiently for Spatial-Temporal Forecasting

• URL: http://arxiv.org/abs/2412.14537v1
• Date: Thu, 19 Dec 2024 05:33:55 GMT
• Title: ST-ReP: Learning Predictive Representations Efficiently for Spatial-Temporal Forecasting
• Authors: Qi Zheng, Zihao Yao, Yaying Zhang,
• Abstract summary: Self-supervised methods are increasingly adapted to learn spatial-temporal representations. Current value reconstruction and future value prediction are integrated into the pre-training framework. Multi-time scale analysis is incorporated into the self-supervised loss to enhance predictive capability.
• Score: 7.637123047745445
• License:
• Abstract: Spatial-temporal forecasting is crucial and widely applicable in various domains such as traffic, energy, and climate. Benefiting from the abundance of unlabeled spatial-temporal data, self-supervised methods are increasingly adapted to learn spatial-temporal representations. However, it encounters three key challenges: 1) the difficulty in selecting reliable negative pairs due to the homogeneity of variables, hindering contrastive learning methods; 2) overlooking spatial correlations across variables over time; 3) limitations of efficiency and scalability in existing self-supervised learning methods. To tackle these, we propose a lightweight representation-learning model ST-ReP, integrating current value reconstruction and future value prediction into the pre-training framework for spatial-temporal forecasting. And we design a new spatial-temporal encoder to model fine-grained relationships. Moreover, multi-time scale analysis is incorporated into the self-supervised loss to enhance predictive capability. Experimental results across diverse domains demonstrate that the proposed model surpasses pre-training-based baselines, showcasing its ability to learn compact and semantically enriched representations while exhibiting superior scalability.

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