Graph Attention NetworkSpatial-temporal graphTemporal domainGait is a kind of attractive feature for human identification at a distance. It can be regarded as a kind of temporal signal. At the same time the human body shape can be regarded as the signal in the spatial domain. In the proposed...
9. 思考 本文使用的DTW算法应该还可以使用其他的计算时间序列相似的算法,比如shapelets等,并且这里的邻接矩阵中都是1,那么默认了节点的重要程度是一致的,其实也可以参考attention的方法,对节点的重要程度进行加权。
论文笔记《Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting》,程序员大本营,技术文章内容聚合第一站。
Yu B, Yin H, Zhu Z (2018) Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. In: Proceedings of the International Joint Conference on Artificial Intelligence, Stockholm, Sweden, pp 3634–3640 Guo S, Lin Y, Feng N, et al (2019) Attention based ...
Attention Based Spatial-Temporal Graph Convolutional Networks ASTGCN模型的总体框架。它由三个具有相同结构的独立组件组成,分别用于对历史数据的最近、日周期和周周期依赖性进行建模。 假设采样频率为每天采样q次,当前时间为t_0,预测窗口大小为T_p,我们截取时间轴上长度为T_h、T_d和T_w的三个时间序列段,分别作为...
2 Rethinking Attention Mechanism for Spatio-Temporal Modeling: A Decoupling Perspective in Traffic Flow Prediction 作者:Qi Yu,Weilong Ding,Hao Zhang,Yang Yang,Tianpu Zhang 关键词:交通预测,解耦 3 Causality-Aware Spatiotemporal Graph Neural Networks for Spatiotemporal Time Series Imputation ...
graph attention network (GAT)self-adaptive adjacency matrixspatial-temporal correlationtraffic speed prediction2024 Inst. of Scientific and Technical Information of China. All rights reserved.Considering the nonlinear structure and spatial-temporal correlation of traffic network, and the influence of...
The pioneering work27 concerning spatial-temporal graph convolutional networks (ST-GCNs), which encapsulate human skeleton data within graph frameworks, is particularly important. In this approach, a GCN is used for skeleton-based action recognition. This impetus has pushed GCN-based methods to the ...
Accurate highway traffic forecasting is a critical task in intelligent transportation systems (ITSs), which needs to capture complex spatiotemporal depende
Zhang X, Huang C, Xu Y, Xia L (2020) Spatial-temporal convolutional graph attention networks for citywide traffic flow forecasting. In: international conference on information and knowledge management (CIKM) Zhang X, Huang C, Xu Y, Xia L, and others (2021) Traffic flow forecasting with spat...