论文标题
GTRANS：时空自动回归变压器，带有图形嵌入到现状的极端事件
GTrans: Spatiotemporal Autoregressive Transformer with Graph Embeddings for Nowcasting Extreme Events
论文作者
论文摘要
时空时间序列的现象应以从模型产生新序列的意义上保留时间和空间动力学，这尊重历史的协方差关系。传统的特征提取器是使用深卷积神经网络（CNN）构建的。但是，CNN模型对图像样应用程序有限制，其中可以使用高维数组形成数据。相比之下，在社交网络，道路交通，物理和化学性质预测中的应用，可以通过图表和图表来组织数据功能。变压器体系结构是一种用于预测模型的新兴方法，由于注意机制设计带来了高精度和效率。本文提出了一个时空模型，即GTRAN，该模型将数据特征转换为图形嵌入，并使用变压器模型预测时间动力学。根据我们的实验，我们证明了GTRAN可以建模空间和时间动态，并为数据集的极端事件进行了现状。此外，在所有实验中，与基线模型相比，GTRAN在二进制级预测测试中均可获得最高的F1和F2分数。
Spatiotemporal time series nowcasting should preserve temporal and spatial dynamics in the sense that generated new sequences from models respect the covariance relationship from history. Conventional feature extractors are built with deep convolutional neural networks (CNN). However, CNN models have limits to image-like applications where data can be formed with high-dimensional arrays. In contrast, applications in social networks, road traffic, physics, and chemical property prediction where data features can be organized with nodes and edges of graphs. Transformer architecture is an emerging method for predictive models, bringing high accuracy and efficiency due to attention mechanism design. This paper proposes a spatiotemporal model, namely GTrans, that transforms data features into graph embeddings and predicts temporal dynamics with a transformer model. According to our experiments, we demonstrate that GTrans can model spatial and temporal dynamics and nowcasts extreme events for datasets. Furthermore, in all the experiments, GTrans can achieve the highest F1 and F2 scores in binary-class prediction tests than the baseline models.
