[41] C. Zhang, D. Song, C. Huang, A. Swami, and N. V. Chawla. Heterogeneous graph neural network. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), pages 793–803, 2019. [42] M. Zhang and Y. Chen. Link prediction based on...
EASSY 1 Temporal Relational Ranking for Stock Prediction This eassy contributed a new deep learning solution, named Relational Stock Ranking (RSR), for stock prediction. The key novelty of this work is the proposal of a new component in neural network modeling, named Temporal Graph Convolution, ...
& Kou, G. Stock Movement Prediction Based on Bi-Typed Hybrid-Relational Market Knowledge Graph Via Dual Attention Networks. IEEE Transactions on Knowledge and Data Engineering (2022).REFERENCES编辑:王菁 数据派研究部介绍 数据派研究部成立...
12.Zhao, Y., Du, H., Liu, Y., Wei, S., Chen, X., Zhuang, F., Li, Q. & Kou, G. Stock Movement Prediction Based on Bi-Typed Hybrid-Relational Market Knowledge Graph Via Dual Attention Networks. IEEE Transactions on Knowledge and Data Engineering (2022).REFERENCES...
12.Zhao, Y., Du, H., Liu, Y., Wei, S., Chen, X., Zhuang, F., Li, Q. & Kou, G. Stock Movement Prediction Based on Bi-Typed Hybrid-Relational Market Knowledge Graph Via Dual Attention Networks. IEEE Transactions on Knowledge and Data Engineering (2022).REFERENCES ...
Long–short-term memory neural networkStock price predictionLeading indicatorsIn today's society, investment wealth management has become a mainstream of the contemporary era. Investment wealth management refers to the use of funds by investors to arrange funds reasonably, for example, savings, bank ...
In addition, downstream tasks include many domain-related end-to-end prediction tasks in addition to classical node classification, link prediction and graph classification. In practical applications, different business scenarios have different requirements for the graph neural network model and downstream ...
examples. We then used this data to train a graph neural network based on the geometric vector perceptron graph neural network (GVP-GNN)31as well as a 3D convolutional neural network (3D-CNN)32. Both these architectures have demonstrated robust performance on other protein structure prediction ...
现代机器学习:with the booming of artificial intelligence technology, machine learning techniques have been introduced to handle complex financial market data and proved to be useful for making stock trend predictions 。 第三段:CNN简介 —— 近些年来使用图像特征的研究 —— 指出现在的不足就是欠缺考虑整...
Graph-based innovations for financial applications, e.g., stock market prediction; Graph-based innovations for communication networks, e.g., intrusion detection, mobile traffic prediction; Graph-based innovations for transportation applications, e.g., road traffic prediction, traffic data imputation; Gra...