Multi-Factor Spatio-Temporal Prediction based on Graph Decomposition Learning
arXiv (Cornell University)(2023)
摘要
Spatio-temporal (ST) prediction is an important and widely used technique in
data mining and analytics, especially for ST data in urban systems such as
transportation data. In practice, the ST data generation is usually influenced
by various latent factors tied to natural phenomena or human socioeconomic
activities, impacting specific spatial areas selectively. However, existing ST
prediction methods usually do not refine the impacts of different factors, but
directly model the entangled impacts of multiple factors. This amplifies the
modeling complexity of ST data and compromises model interpretability. To this
end, we propose a multi-factor ST prediction task that predicts partial ST data
evolution under different factors, and combines them for a final prediction. We
make two contributions to this task: an effective theoretical solution and a
portable instantiation framework. Specifically, we first propose a theoretical
solution called decomposed prediction strategy and prove its effectiveness from
the perspective of information entropy theory. On top of that, we instantiate a
novel model-agnostic framework, named spatio-temporal graph decomposition
learning (STGDL), for multi-factor ST prediction. The framework consists of two
main components: an automatic graph decomposition module that decomposes the
original graph structure inherent in ST data into subgraphs corresponding to
different factors, and a decomposed learning network that learns the partial ST
data on each subgraph separately and integrates them for the final prediction.
We conduct extensive experiments on four real-world ST datasets of two types of
graphs, i.e., grid graph and network graph. Results show that our framework
significantly reduces prediction errors of various ST models by 9.41
average (35.36
interpretability potential of our framework.
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关键词
graph decomposition,prediction,multi-factor,spatio-temporal
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