In many problem settings that require spatio-temporal forecasting, the values in the time-series not only exhibit spatio-temporal correlations but are also influenced by spatial diffusion across locations. One such example is forecasting the concentration of fine particulate matter (PM2.5) in the atmosphere which is influenced by many complex factors, the most important ones being diffusion due to meteorological factors as well as transport across vast distances over a period of time. We present a novel Spatio-Temporal Graph Neural Network architecture, that specifically captures these dependencies to forecast the PM2.5 concentration. Our model is based on an encoder-decoder architecture where the encoder and decoder parts leverage gated recurrent units (GRU) augmented with a graph neural network (TransformerConv) to account for spatial diffusion. Our model can also be seen as a generalization of various existing models for time-series or spatio-temporal forecasting. We demonstrate the model's effectiveness on two real-world PM2.5 datasets: (1) data collected by us using a recently deployed network of low-cost PM$_{2.5}$ sensors from 511 locations spanning the entirety of the Indian state of Bihar over a period of one year, and (2) another publicly available dataset that covers severely polluted regions from China for a period of 4 years. Our experimental results show our model's impressive ability to account for both spatial as well as temporal dependencies precisely.

翻译：在许多需要时空预测的问题场景中，时间序列中的数值不仅表现出时空相关性，还受到跨位置空间扩散的影响。一个典型例子是大气中细颗粒物（PM2.5）浓度的预测，其受众多复杂因素影响，其中最重要的因素包括气象因素导致的扩散以及随时间推移在远距离范围内的传输。本文提出一种新颖的时空图神经网络架构，专门捕捉这些依赖关系以预测PM2.5浓度。我们的模型基于编码器-解码器架构，其中编码器和解码器部分利用门控循环单元（GRU）并结合图神经网络（TransformerConv）来建模空间扩散效应。该模型也可视为对现有多种时间序列或时空预测模型的泛化。我们在两个真实世界的PM2.5数据集上验证了模型的有效性：（1）使用近期部署的低成本PM$_{2.5}$传感器网络在印度比哈尔邦全境511个地点采集的为期一年的数据；（2）另一个公开可用的涵盖中国重污染区域为期四年的数据集。实验结果表明，我们的模型能够精确处理空间与时间依赖关系，展现出卓越的预测能力。