<ul><li>Spatial-temporal forecasting is crucial in various domains.</li><li>Challenges in self-supervised learning for spatial-temporal forecasting include selecting reliable negative pairs, overlooking spatial correlations, and limitations of efficiency and scalability.</li><li>ST-ReP is a lightweight representation-learning model that integrates current value reconstruction and future value prediction.</li><li>ST-ReP surpasses pre-training-based baselines and exhibits superior scalability.</li></ul>

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

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