Transmission characteristics and domain knowledge of infectious diseases should be further applied to the design of deep learning models and feature selection.ConclusionThe ATGCN model addressed the multivariate time series forecasting in a graph-based deep learning approach and achieved robust prediction ...
Traditional solutions for stock prediction: based on time-series models 之前方法的缺陷:一般预测估计股价都是用分类和回归两种方法;把股票当成相互独立的个体。 本文的方法:RSR 本文的创新点:在神经网络中加入Temporal Graph Convolution Introduction 传统股价预测方法:把股价当成随机过程并且把历史数据(indicators)作为...
Sandwich structure: two grated sequential convolution layers & one spatial graph convolution layer 3.2 Graph CNNs for Extracting Spatial Features Spectral的方法\Theta *_{\mathcal{G}} x=\Theta(L) x=\Theta\left(U \Lambda U^{T}\right) x=U \Theta(\Lambda) U^{T} x, 计算昂贵,所以考虑下面...
The code and models of ST-GCN are made publicly availablehttps://github.com/yysijie/st-gcn. 将GCNs 拓展到 graph 上主要有如下两大类方法: 1). thespectral perspective, wherethe locality of graph convolutionis considered in the form ofspectral analysis; 2). thespatial perspective, where the c...
spatiotemporal models integrating graph convolutional networks and recurrent neural networks have become traffic forecasting research hotspots and have made significant progress. However, few works integrate external factors. Therefore, based on the assumption that introducing external factors can enhance the ...
摘要: Contents·Prediction Problems in Traffic·Data-driven methods/Deep learning·Graph Convolution Operation·Spatio-Temporal Graph Convolutional Networks·Experiments and Results·Summary & Beyond Prediction Problems in Traffic 会议名称: 第五届交通科学与计算专题研讨会 会议地点: 贵阳 收藏...
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021) deep-learningnetwork-sciencepytorchtemporal-networksspatial-analysisspatial-dataspatiotemporalnetwork-embeddingspatio-temporal-analysisgraph-convolutional-networksgcnspatio-temporal-datatemporal-datagraph-embedding...
The proposed model is generic and principled as it can be generalized into other dynamic models. We theoretically prove the stability of STGC and provide an upper-bound of the signal transformation to be learnt. Further, the proposed recursive model can be stacked into a multi-layer architecture...
Dai Z, Yang Z, Yang Y, Carbonell J, Le QV, Salakhutdinov R (2019) Transformer-xl: attentive language models beyond a fixed-length context. arXiv preprint arXiv:1901.02860 Fu J, Liu J, Tian H, Li Y, Bao Y, Fang Z, Lu H (2019) Dual attention network for scene segmentation. In: ...
n_route=228, graph='default', ks=3, kt=3, n_his=12, n_pred=9 batch_size=50, epoch=50, lr=0.001, opt='RMSProp', inf_mode='merge', save=10 Data source will be searched in dataset_dir = './dataset', including speed records and the weight matrix. Trained models will be saved...