A Simple Framework for Multi-mode Spatial-Temporal Data Modeling
This work addresses the challenge of efficiently modeling multi-mode spatial-temporal data, which is incremental as it builds on existing methods by simplifying complex components.
The paper tackles the problem of modeling multi-mode spatial-temporal data by proposing a simple framework that adaptively learns cross-mode spatial relationships and captures temporal dependencies with multi-layer perceptrons, achieving consistent performance improvements over baselines on three real-world datasets with lower space and time complexity.
Spatial-temporal data modeling aims to mine the underlying spatial relationships and temporal dependencies of objects in a system. However, most existing methods focus on the modeling of spatial-temporal data in a single mode, lacking the understanding of multiple modes. Though very few methods have been presented to learn the multi-mode relationships recently, they are built on complicated components with higher model complexities. In this paper, we propose a simple framework for multi-mode spatial-temporal data modeling to bring both effectiveness and efficiency together. Specifically, we design a general cross-mode spatial relationships learning component to adaptively establish connections between multiple modes and propagate information along the learned connections. Moreover, we employ multi-layer perceptrons to capture the temporal dependencies and channel correlations, which are conceptually and technically succinct. Experiments on three real-world datasets show that our model can consistently outperform the baselines with lower space and time complexity, opening up a promising direction for modeling spatial-temporal data. The generalizability of the cross-mode spatial relationships learning module is also validated.