Spatial-temporal forecasting plays an important role in many real-world applications, such as traffic forecasting, air pollutant forecasting, crowd-flow forecasting, and so on. State-of-the-art spatial-temporal forecasting models take data-driven approaches and rely heavily on data availability. Such models suffer from accuracy issues when data is incomplete, which is common in reality due to the heavy costs of deploying and maintaining sensors for data collection. A few recent studies attempted to address the issue of incomplete data. They typically assume some data availability in a region of interest either for a short period or at a few locations. In this paper, we further study spatial-temporal forecasting for a region of interest without any historical observations, to address scenarios such as unbalanced region development, progressive deployment of sensors or lack of open data. We propose a model named STSM for the task. The model takes a contrastive learning-based approach to learn spatial-temporal patterns from adjacent regions that have recorded data. Our key insight is to learn from the locations that resemble those in the region of interest, and we propose a selective masking strategy to enable the learning. As a result, our model outperforms adapted state-of-the-art models, reducing errors consistently over both traffic and air pollutant forecasting tasks. The source code is available at https://github.com/suzy0223/STSM.

翻译：空间-时间预测在诸多实际应用中扮演着重要角色，例如交通预测、空气污染物预测、人群流动预测等。当前最先进的空间-时间预测模型采用数据驱动方法，高度依赖数据的可用性。然而，由于部署和维护数据采集传感器的高昂成本，实际场景中数据不完整的情况普遍存在，此类模型在数据缺失时容易出现精度问题。近期的一些研究尝试解决数据不完整问题，它们通常假设目标区域在短时间内或少数位置存在部分可用数据。本文进一步研究了在没有任何历史观测数据的目标区域中进行空间-时间预测的问题，以应对区域发展不平衡、传感器逐步部署或开放数据缺乏等场景。我们针对该任务提出了一种名为STSM的模型。该模型采用基于对比学习的方法，从具有记录数据的相邻区域中学习空间-时间模式。关键见解在于从与目标区域相似的测点中学习特征，并设计了一种选择性遮蔽策略来实现这一学习过程。实验结果表明，我们的模型优于改造后的现有最先进模型，在交通预测和空气污染物预测任务中均能持续降低误差。源代码已发布于https://github.com/suzy0223/STSM。