  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.
