论文标题
空间 - 周期性同步图形变压器网络（STSGT）
Spatial-Temporal Synchronous Graph Transformer network (STSGT) for COVID-19 forecasting
论文作者
论文摘要
在过去的几年中，Covid-19已成为严重关注的问题。它对全球范围内的许多人产生了不利影响，并导致了数十亿美元的商业资本损失。在本文中，我们提出了一种新型的时空同步图形变压器网络（STSGT），以捕获COVID-19时间序列数据的复杂空间和时间依赖性，并预测不断发展的大流行的未来状态。 STSGT的层将图形卷积网络（GCN）与在同步的空间 - 周期图上的变压器的自发机制相结合，以捕获COVID时间序列的动态变化模式。时空同步图同时捕获了在给定和随后的时间段的图形顶点之间的空间和时间依赖性，这有助于捕获时间序列中的异质性并提高预测准确性。我们对两个公开可用的现实世界共互联-19时间序列数据集进行了广泛的实验，这表明，STSGT明显胜过为空间暂时性预测任务而设计的最先进算法。 Specifically, on average over a 12-day horizon, we observe a potential improvement of 12.19% and 3.42% in Mean Absolute Error(MAE) over the next best algorithm while forecasting the daily infected and death cases respectively for the 50 states of US and Washington, D.C. Additionally, STSGT also outperformed others when forecasting the daily infected cases at the state level, e.g., for all the counties in the State of密歇根州。代码和模型可在https://github.com/soumbane/stsgt上公开获取。
COVID-19 has become a matter of serious concern over the last few years. It has adversely affected numerous people around the globe and has led to the loss of billions of dollars of business capital. In this paper, we propose a novel Spatial-Temporal Synchronous Graph Transformer network (STSGT) to capture the complex spatial and temporal dependency of the COVID-19 time series data and forecast the future status of an evolving pandemic. The layers of STSGT combine the graph convolution network (GCN) with the self-attention mechanism of transformers on a synchronous spatial-temporal graph to capture the dynamically changing pattern of the COVID time series. The spatial-temporal synchronous graph simultaneously captures the spatial and temporal dependencies between the vertices of the graph at a given and subsequent time-steps, which helps capture the heterogeneity in the time series and improve the forecasting accuracy. Our extensive experiments on two publicly available real-world COVID-19 time series datasets demonstrate that STSGT significantly outperforms state-of-the-art algorithms that were designed for spatial-temporal forecasting tasks. Specifically, on average over a 12-day horizon, we observe a potential improvement of 12.19% and 3.42% in Mean Absolute Error(MAE) over the next best algorithm while forecasting the daily infected and death cases respectively for the 50 states of US and Washington, D.C. Additionally, STSGT also outperformed others when forecasting the daily infected cases at the state level, e.g., for all the counties in the State of Michigan. The code and models are publicly available at https://github.com/soumbane/STSGT.
