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% on
average (35.36% at most). Furthermore, a case study reveals the
interpretability potential of our framework.

我们提出了一个多因素时空预测任务，预测不同因素下局部时空数据的演变，并将它们结合起来进行最终预测。通过理论解决方案和可移植的实例化框架，我们在这个任务中做出了两个贡献：提出了一种称为分解预测策略的有效理论解决方案，并从信息熵理论的角度证明了其有效性。在此基础上，我们实例化了一个新颖的模型无关框架，名为时空图分解学习（STGDL），用于多因素时空预测。框架包括两个主要组件：自动图分解模块，将时空数据中固有的原始图结构分解为对应于不同因素的子图，以及分解学习网络，分别学习每个子图上的局部时空数据，并将它们整合为最终预测。我们对两种类型的四个真实世界时空数据集进行大量实验，即网格图和网络图。结果表明我们的框架平均显著减少了各种时空模型的预测误差（最多达到 35.36% 的减少）。此外，一项案例研究揭示了我们框架的可解释性潜力。