When do contrastive learning signals help spatio-temporal graph forecasting?

Deep learning models are modern tools for spatio-temporal graph (STG) forecasting. Though successful, we argue that data scarcity is a key factor limiting their recent improvements. Meanwhile, contrastive learning has been an effective method for providing self-supervision signals and addressing data scarcity in various domains. In view of this, one may ask: can we leverage the additional signals from contrastive learning to alleviate data scarcity, so as to benefit STG forecasting? To answer this question, we present the first systematic exploration on incorporating contrastive learning into STG forecasting. Specifically, we first elaborate two potential schemes for integrating contrastive learning. We then propose two feasible and efficient designs of contrastive tasks that are performed on the node or graph level. The empirical study on STG benchmarks demonstrates that integrating graph-level contrast with the joint learning scheme achieves the best performance. In addition, we introduce four augmentations for STG data, which perturb the data in terms of graph structure, time domain, and frequency domain. Experimental results reveal that the model is not sensitive to the proposed augmentations' semantics. Lastly, we extend the classic contrastive loss via a rule-based strategy that filters out the most semantically similar negatives, yielding performance gains. We also provide explanations and insights based on the above experimental findings. Code is available at https://github.com/liuxu77/STGCL.