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

arXiv 2308.11204 ↗First posted 22 Aug 2023cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 35 references that are in the map. First posted on arXiv 22 Aug 2023.

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22 of 35 references.

References · 22

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10 more
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