  Spatiotemporal systems are common in the real-world. Forecasting the
multi-step future of these spatiotemporal systems based on the past
observations, or, Spatiotemporal Sequence Forecasting (STSF), is a significant
and challenging problem. Although lots of real-world problems can be viewed as
STSF and many research works have proposed machine learning based methods for
them, no existing work has summarized and compared these methods from a unified
perspective. This survey aims to provide a systematic review of machine
learning for STSF. In this survey, we define the STSF problem and classify it
into three subcategories: Trajectory Forecasting of Moving Point Cloud
(TF-MPC), STSF on Regular Grid (STSF-RG) and STSF on Irregular Grid (STSF-IG).
We then introduce the two major challenges of STSF: 1) how to learn a model for
multi-step forecasting and 2) how to adequately model the spatial and temporal
structures. After that, we review the existing works for solving these
challenges, including the general learning strategies for multi-step
forecasting, the classical machine learning based methods for STSF, and the
deep learning based methods for STSF. We also compare these methods and point
out some potential research directions.
