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We propose an end-to-end architecture for multivariate time-series prediction that integrates a spatial-temporal graph neural network with a matrix filtering module.
Portfolio Selection
H. Markowitz. 1952 · 1952
Earlier work this paper cites.
On the shortest spanning subtree of a graph and the traveling salesman problem
Joseph B. Kruskal. 1956 · 1956
Earlier work this paper cites.
Shortest connection networks and some generalizations
Robert C. Prim. 1957 · 1957
Earlier work this paper cites.
Time series analysis, forecasting and control
P. J. Young and Stephen Shellswell. 1972 · 1972
Earlier work this paper cites.
Maximum Likelihood Estimation for Vector Autoregressive Moving Average Models
Theodore W. Anderson. 1978 · 1978
Earlier work this paper cites.
Learning internal representations by error propagation
David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams. 1986 · 1986
Earlier work this paper cites.
Otakar Boruvka on minimum spanning tree problem Translation of both the 1926 papers, comments, history
Jaroslav Nešetřil, Eva Milková, and Helena Nešetřilová. 2001 · 2001
Earlier work this paper cites.
Honey, I Shrunk the Sample Covariance Matrix
Olivier Ledoit and Michael Wolf. 2003 · 2003
Earlier work this paper cites.
A well-conditioned estimator for large-dimensional covariance matrices
Olivier Ledoit and Michael Wolf. 2004 · 2004
Earlier work this paper cites.
A tool for filtering information in complex systems
M. Tumminello, T. Aste, T. Di Matteo, and R. N. Mantegna. 2005 · 2005
Earlier work this paper cites.
Learning with Tensor Representation
Deng Cai, Xiaofei He, and Jiawei Han. 2006 · 2006
Earlier work this paper cites.
High-dimensional graphs and variable selection with the Lasso
Nicolai Meinshausen and Peter Buhlmann. 2006 · 2006
Earlier work this paper cites.
Model Selection Through Sparse Maximum Likelihood Estimation
Onureena Banerjee, Laurent El Ghaoui, and Alexandre d’Aspremont. 2007 · 2007
Earlier work this paper cites.
Model selection and estimation in the Gaussian graphical model
Ming Yuan and Yi Lin. 2007 · 2007
Earlier work this paper cites.
Neural Network with Matrix Inputs
Povilas Daniusis and Pranas Vaitkus. 2008 · 2008
Earlier work this paper cites.
Sparse inverse covariance estimation with the graphical lasso
J. Friedman, T. Hastie, and R. Tibshirani. 2008 · 2008
Earlier work this paper cites.
Shrinkage Algorithms for MMSE Covariance Estimation
Yilun Chen, Ami Wiesel, Yonina C. Eldar, and Alfred O. Hero. 2010 · 2010
Earlier work this paper cites.
Covariance, correlation matrix, and the multiscale community structure of networks
Huawei Shen, Xue qi Cheng, and Bin xing Fang. 2010 · 2010
Earlier work this paper cites.
Scikit-learn: Machine Learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011 · 2011
Earlier work this paper cites.
The Brain as a Complex System: Using Network Science as a Tool for Understanding the Brain
Qawi K. Telesford, S. Simpson, J. Burdette, S. Hayasaka, and P. Laurienti. 2011 · 2011
Earlier work this paper cites.
A Comparison of Estimation Methods for Vector Autoregressive Moving-Average Models
Christian Kascha. 2012 · 2012
Earlier work this paper cites.
Nonlinear shrinkage estimation of large-dimensional covariance matrices
Olivier Ledoit and Michael Wolf. 2012 · 2012
Earlier work this paper cites.
The Graphical Lasso: New Insights and Alternatives
Rahul Mazumder and Trevor J. Hastie. 2012 · 2012
Earlier work this paper cites.
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
Junyoung Chung, Çaglar Gülçehre, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Large dimensional analysis and optimization of robust shrinkage covariance matrix estimators
Romain Couillet and Matthew R. Mckay. 2014 · 2014
Earlier work this paper cites.
Long short-term memory recurrent neural network architectures for large scale acoustic modeling. In INTERSPEECH
Hasim Sak, Andrew W. Senior, and Françoise Beaufays. 2014 · 2014
Earlier work this paper cites.
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting. In NIPS
Xingjian Shi, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, and Wang chun Woo. 2015 · 2015
Earlier work this paper cites.
Parsimonious modeling with information filtering networks
Wolfram Barfuss, Guido Previde Massara, T. Di Matteo, and Tomaso Aste. 2016 · 2016
Earlier work this paper cites.
Spectral sparsification in spectral clustering
Alireza Chakeri, Hamidreza Farhidzadeh, and Lawrence O. Hall. 2016 · 2016
Earlier work this paper cites.
Sparse Causality Network Retrieval from Short Time Series
T. Aste and T. Matteo. 2017 · 2017
Cited alongside, same era.
Language Modeling with Gated Convolutional Networks. In ICML
Yann Dauphin, Angela Fan, Michael Auli, and David Grangier. 2017 · 2017
Cited alongside, same era.
Junbin Gao, Yi Guo, and Zhiyong Wang. 2017 · 2017
Cited alongside, same era.
Semi-Supervised Classification with Graph Convolutional Networks
Thomas Kipf and Max Welling. 2017 · 2017
Cited alongside, same era.
Network Filtering for Big Data: Triangulated Maximally Filtered Graph
Guido Previde Massara, T. Matteo, and T. Aste. 2017 · 2017
Cited alongside, same era.
Graph WaveNet for Deep Spatial-Temporal Graph Modeling. In IJCAI
Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019 · 2019
Later among the works it cites.
Topological regularization with information filtering networks
Tomaso Aste. 2020 · 2020
Later among the works it cites.
Deep Learning Modeling of the Limit Order Book: A Comparative Perspective
Antonio Briola, Jeremy D. Turiel, and Tomaso Aste. 2020 · 2020
Later among the works it cites.
Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting
Defu Cao, Yujing Wang, Juanyong Duan, Ce Zhang, Xia Zhu, Congrui Huang, Yunhai Tong, Bixiong Xu, Jing Bai, Jie Tong, and Qi Zhang. 2020 · 2020
Later among the works it cites.
Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting
Weiqiu Chen, Ling Chen, Yu Xie, Wei Cao, Yusong Gao, and Xiaojie Feng. 2020 · 2020
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Cited alongside, same era.
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Cited alongside, same era.
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Cited alongside, same era.
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Cited alongside, same era.
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Cited alongside, same era.
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Cited alongside, same era.
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Cited alongside, same era.
Later among the works it cites.
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Charles R. Harris, K. Jarrod Millman, Stéfan J van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, Robert Kern, Matti Picus, Stephan Hoyer, Marten H. van Kerkwijk, Matthew Brett, Allan Haldane, Jaime Fernández del Río, Mark Wiebe, Pearu Peterson, Pierre Gérard-Marchant, Kevin Sheppard, Tyler Reddy, Warren Weckesser, Hameer Abbasi, Christoph Gohlke, and Travis E. Oliphant. 2020 · 2020
Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
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DropEdge: Towards Deep Graph Convolutional Networks on Node Classification. In ICLR
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Later among the works it cites.
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Improved Large Dynamic Covariance Matrix Estimation With Graphical Lasso and Its Application in Portfolio Selection
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T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction
Ling Zhao, Yujiao Song, Chao Zhang, Yu Liu, Pu Wang, Tao Lin, Min Deng, and Haifeng Li. 2020 · 2020
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Deep Reinforcement Learning for Active High Frequency Trading
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Cornelius Fritz, Emilio Dorigatti, and D. Rügamer. 2021 · 2021
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Dynamic Portfolio Optimization with Inverse Covariance Clustering
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