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Deep learning models are modern tools for spatio-temporal graph (STG) forecasting. Despite their effectiveness, they require large-scale datasets to achieve better performance and are vulnerable to noise perturbation. To alleviate these limitations, an intuitive idea is to use the popular data augmentation and contrastive learning techniques. However, existing graph contrastive learning methods cannot be directly applied to STG forecasting due to three reasons. First, we empirically discoverarXiv:2108.11873v1 fatcat:ktkpm3m33fdr5lxglcmqcijpce