A Simple Framework for Multi-mode Spatial-Temporal Data Modeling
Zihang Liu Le Yu Tongyu Zhu Leiei Sun
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.