Combining random forest and graph wavenet for spatial-temporal data prediction
Author:
Affiliation:
1. College of Information Science and Engineering, China University of Petroleum-Beijing,Beijing,China,102249
2. Education Management Information Centre of the Ministry of Education,Beijing,China,100816
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Earth and Planetary Sciences,General Engineering,General Environmental Science
Link
http://xplorestaging.ieee.org/ielx7/9195266/10026509/10026523.pdf?arnumber=10026523
Reference26 articles.
1. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting;li;ArXiv Preprint,2018
2. Graph attention networks;veli?kovi?;ArXiv Preprint,2017
3. GaAN:Gated attention networks for learning on large and spatiotemporal graphs;zhang;ArXiv Preprint,2018
4. Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting
5. Inductive representation learning on large graphs;hamilton;Proc 31st International Conference on Neural Information Processing Systems,2017
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