A Simple Framework for Multi-mode Spatial-Temporal Data Modeling

TL;DR

This paper introduces a simple, effective framework for modeling multi-mode spatial-temporal data, combining adaptive cross-mode relationship learning with neural networks to outperform baselines efficiently.

Contribution

The paper presents a novel, streamlined framework that effectively models multiple spatial-temporal modes using a general cross-mode relationship module and MLPs, reducing complexity.

Findings

Outperforms baseline models on three real-world datasets

Achieves lower space and time complexity

Demonstrates the generalizability of the cross-mode learning module

Abstract

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.