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

• URL: http://arxiv.org/abs/2308.11204v1
• Date: Tue, 22 Aug 2023 05:41:20 GMT
• Title: A Simple Framework for Multi-mode Spatial-Temporal Data Modeling
• Authors: Zihang Liu, Le Yu, Tongyu Zhu, Leiei Sun
• Abstract summary: 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. Experiments on three real-world datasets show that our model can consistently outperform the baselines with lower space and time complexity.
• Score: 4.855443906457102
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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