FDN is a neural forecasting architecture that decomposes future predictions via classification to yield interpretable latent patterns alongside SOTA-level accuracy at reduced memory and runtime cost.
Machine Learning for Spatiotemporal Sequence Forecasting: A Survey
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abstract
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.
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cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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FDN: Interpretable Spatiotemporal Forecasting with Future Decomposition Networks
FDN is a neural forecasting architecture that decomposes future predictions via classification to yield interpretable latent patterns alongside SOTA-level accuracy at reduced memory and runtime cost.