Fetching the paper…
Reading the bibliography…
Time series forecasting is an extensively studied subject in statistics, economics, and computer science.
Statistical inference for probabilistic functions of finite state markov chains
L. E. Baum and T. Petrie · 1966
Earlier work this paper cites.
Investigating causal relations by econometric models and cross-spectral methods
C. W. J. Granger · 1969
Earlier work this paper cites.
Learning representations by back-propagating errors
Ronald J. Williams, Geoffrey E. Hinton, and David E. Rumelhart · 1986
Earlier work this paper cites.
Time Series Analysis
James D. Hamilton · 1994
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Forecasting: Methods and Applications
Spyros G. Makridakis, Steven C. Wheelwright, and Rob J Hyndman · 1997
Earlier work this paper cites.
Bidirectional recurrent neural networks
Mike Schuster and Kuldip K. Paliwal · 1997
Earlier work this paper cites.
Fundamentals of Kalman Filtering
Paul Zarchan and Howard Musoff · 2000
Earlier work this paper cites.
ARIMA models and the Box-Jenkins methodology
Dimitros Asteriou and Stephen G. Hall · 2011
Earlier work this paper cites.
Learning phrase representations using RNN encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart van Merrienboera, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
Earlier work this paper cites.
Big data and its technical challenges
H. V. Jagadish, Johannes Gehrke, Alexandros Labrinidis, Yannis Papakonstantinou, Jignesh M. Patel, Raghu Ramakrishnan, and Cyrus Shahabi · 2014
Earlier work this paper cites.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc Viet Le · 2014
Cited alongside, same era.
Graph neural networks: A review of methods and applications
Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun · 2014
Cited alongside, same era.
Structured sequence modeling with graph convolutional recurrent networks
Youngjoo Seo, Michaël Defferrard, Pierre Vandergheynst, and Xavier Bresson · 2016
Cited alongside, same era.
Forward and reverse gradient-based hyperparameter optimization
Luca Franceschi, Michele Donini, Paolo Frasconi, and Massimiliano Pontil · 2017
Cited alongside, same era.
Categorical reparameterization with Gumbel-Softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2017
Cited alongside, same era.
DAGs with NO TEARS: Continuous optimization for structure learning
Xun Zheng, Bryon Aragam, Pradeep Ravikumar, and Eric P. Xing · 2018
Later among the works it cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Later among the works it cites.
Learning discrete structures for graph neural networks
Luca Franceschi, Mathias Niepert, Massimiliano Pontil, and Xiao He · 2019
Later among the works it cites.
Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting
Shiyang Li, Xiaoyong Jin, Yao Xuan, Xiyou Zhou, Wenhu Chen, Yu-Xiang Wang, and Xifeng Yan · 2019
Later among the works it cites.
Temporal pattern attention for multivariate time series forecasting
Shun-Yao Shih, Fan-Keng Sun, and Hung yi Lee · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
The concrete distribution: A continuous relaxation of discrete random variables
Chris J. Maddison, Andriy Mnih, and Yee Whye Teh · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Neural relational inference for interacting systems
Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, and Richard Zemel · 2018
Cited alongside, same era.
Diffusion convolutional recurrent neural network: Data-driven traffic forecasting
Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu · 2018
Cited alongside, same era.
Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting
Bing Yu, Haoteng Yin, and Zhanxing Zhu · 2018
Cited alongside, same era.
Deep learning on graphs: A survey
Ziwei Zhang, Peng Cui, and Wenwu Zhu · 2018
Cited alongside, same era.
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S. Yu · 2019
Later among the works it cites.
DAG-GNN: DAG structure learning with graph neural networks
Yue Yu, Jie Chen, Tian Gao, and Mo Yu · 2019
Later among the works it cites.
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 · 2019
Later among the works it cites.
Gradient-based neural DAG learning
Sébastien Lachapelle, Philippe Brouillard, Tristan Deleu, and Simon Lacoste-Julien · 2020
Later among the works it cites.
Connecting the dots: Multivariate time series forecasting with graph neural networks
Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Xiaojun Chang, and Chengqi Zhang · 2020
Later among the works it cites.