论文标题
关于包括时空神经网络的空间信息
On the Inclusion of Spatial Information for Spatio-Temporal Neural Networks
论文作者
论文摘要
在面对时空回归时，可以为模型提供有关空间维度的任何可用的先验信息，这是明智的。例如，通常基于空间亲密，邻接或相关性来定义神经网络的结构。如果空间信息不可用，或者在模型中引入该空间信息太高，则是一种常见的替代方案，就是将其作为模型的额外步骤。尽管使用或学习的先前空间知识的使用可能是有益的，但在这项工作中，我们通过将空间不可知论神经网络与最先进的模型进行比较来质疑这一原则。我们的结果表明，在大多数情况下，实际上并不需要典型的先前空间信息。为了验证这一违反直觉结果，我们对与可持续移动性和空气质量相关的十个不同数据集进行了彻底的实验，从而证实了我们对现实世界中问题的结论，对公共卫生和经济有直接影响。
When confronting a spatio-temporal regression, it is sensible to feed the model with any available prior information about the spatial dimension. For example, it is common to define the architecture of neural networks based on spatial closeness, adjacency, or correlation. A common alternative, if spatial information is not available or is too costly to introduce it in the model, is to learn it as an extra step of the model. While the use of prior spatial knowledge, given or learnt, might be beneficial, in this work we question this principle by comparing spatial agnostic neural networks with state of the art models. Our results show that the typical inclusion of prior spatial information is not really needed in most cases. In order to validate this counterintuitive result, we perform thorough experiments over ten different datasets related to sustainable mobility and air quality, substantiating our conclusions on real world problems with direct implications for public health and economy.
