Embedding Enhanced MLP Enables Simple and Extensible Spatiotemporal Forecasting

Spatiotemporal forecasting facilitates many real world intelligent systems. Combining graph learning with temporal models has recently become popular in spatiotemporal forecasting. Although graph convolution enhances the modeling of spatial correlations, it results in unsatisfactory efficiency and poor extensibility in existing models. Consequently, existing graph-learning-based methods struggle to handle emerging nodes and are difficult to deploy on large-scale datasets. In this paper, we argue that graph learning in spatiotemporal forecasting is to discriminate different nodes thus generating node-specific features. To achieve the same effect of graph learning, we propose to use node embeddings to learn node-specific features and introduce a simple yet well-performing Embedding Enhanced MLP (E2MLP) spatiotemporal forecasting model. A hierarchical MLP framework is proposed, where node embeddings are introduced in each procedure of the framework. E2MLP outperforms existing SOTA spatiotemporal forecasting models on five real-world widely-used datasets, and can be easily scaled to newly emerging nodes without any performance influence on existing nodes. The source codes are publicly available from https://github.com/Ziyan2019/E2MLP