Fetching the paper…
Reading the bibliography…
The key problem in multivariate time series (MTS) analysis and forecasting aims to disclose the underlying couplings between variables that drive the co-movements.
An introduction to harmonic analysis
Y. Katznelson · 1970
Earlier work this paper cites.
Vector autoregressions and cointegration
Mark W. Watson · 1993
Earlier work this paper cites.
Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
Earlier work this paper cites.
Forecasting with exponential smoothing
R. Hyndman, A. Koehler, K. Ord, and R. Snyder · 2008
Earlier work this paper cites.
Arima models and the box–jenkins methodology
Dimitros Asteriou and Stephen G Hall · 2011
Earlier work this paper cites.
Discrete signal processing on graphs
Aliaksei Sandryhaila and José M. F. Moura · 2013
Earlier work this paper cites.
Forecasting fine-grained air quality based on big data
Yu Zheng, Xiuwen Yi, Ming Li, Ruiyuan Li, Zhangqing Shan, Eric Chang, and Tianrui Li · 2015
Earlier work this paper cites.
Conditional time series forecasting with convolutional neural networks
A. Borovykh, S. Bohte, and C. W. Oosterlee · 2017
Earlier work this paper cites.
Blind identification of graph filters
Santiago Segarra, Gonzalo Mateos, Antonio G. Marques, and Alejandro Ribeiro · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Stock price prediction via discovering multi-frequency trading patterns
Liheng Zhang, Charu C. Aggarwal, and Guo-Jun Qi · 2017
Earlier work this paper cites.
An empirical evaluation of generic convolutional and recurrent networks for sequence modeling
Shaojie Bai, J. Zico Kolter, and Vladlen Koltun · 2018
Earlier work this paper cites.
Neural relational inference for interacting systems
Thomas N. Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, and Richard S. Zemel · 2018
Earlier work this paper cites.
Modeling long- and short-term temporal patterns with deep neural networks
Guokun Lai, Wei-Cheng Chang, Yiming Yang, and Hanxiao Liu · 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.
Multilevel wavelet decomposition network for interpretable time series analysis
Jingyuan Wang, Ze Wang, Jianfeng Li, and Junjie Wu · 2018
Cited alongside, same era.
The UCR time series archive
Hoang Anh Dau, Anthony J. Bagnall, Kaveh Kamgar, Chin-Chia Michael Yeh, Yan Zhu, Shaghayegh Gharghabi, Chotirat Ann Ratanamahatana, and Eamonn J. Keogh · 2019
Cited alongside, same era.
Diffusion improves graph learning
Johannes Klicpera, Stefan Weißenberger, and Stephan Günnemann · 2019
Cited alongside, same era.
Connecting the dots: Identifying network structure via graph signal processing
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.
Learning parametrised graph shift operators
George Dasoulas, Johannes F. Lutzeyer, and Michalis Vazirgiannis · 2021
Later among the works it cites.
Graph neural network-based anomaly detection in multivariate time series
Ailin Deng and Bryan Hooi · 2021
Later among the works it cites.
Edgenets: Edge varying graph neural networks
Elvin Isufi, Fernando Gama, and Alejandro Ribeiro · 2021
Later among the works it cites.
Fourier neural operator for parametric partial differential equations
Zongyi Li, Nikola Borislavov Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew M. Stuart, and Anima Anandkumar · 2021
Later among the works it cites.
Time-series forecasting with deep learning: a survey
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Gonzalo Mateos, Santiago Segarra, Antonio G. Marques, and Alejandro Ribeiro · 2019
Cited alongside, same era.
Think globally, act locally: A deep neural network approach to high-dimensional time series forecasting
Rajat Sen, Hsiang-Fu Yu, and Inderjit S. Dhillon · 2019
Cited alongside, same era.
Graph wavenet for deep spatial-temporal graph modeling
Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang · 2019
Cited alongside, same era.
Adaptive graph convolutional recurrent network for traffic forecasting
Lei Bai, Lina Yao, Can Li, Xianzhi Wang, and Can Wang · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Fast fourier convolution
Lu Chi, Borui Jiang, and Yadong Mu · 2020
Cited alongside, same era.
Stability of graph neural networks to relative perturbations
Fernando Gama, Alejandro Ribeiro, and Joan Bruna · 2020
Cited alongside, same era.
B. Lim and S. Zohren · 2021
Later among the works it cites.
Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting
Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long · 2021
Later among the works it cites.
Informer: Beyond efficient transformer for long sequence time-series forecasting
Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang · 2021
Later among the works it cites.
TAMP-s2GCNets: Coupling time-aware multipersistence knowledge representation with spatio-supra graph convolutional networks for time-series forecasting
Yuzhou Chen, Ignacio Segovia-Dominguez, Baris Coskunuzer, and Yulia Gel · 2022
Closest in time.
Adaptive fourier neural operators: Efficient token mixers for transformers
John Guibas, Morteza Mardani, Zongyi Li, Andrew Tao, Anima Anandkumar, and Bryan Catanzaro · 2022
Closest in time.
Space-time graph neural networks
Samar Hadou, Charilaos I Kanatsoulis, and Alejandro Ribeiro · 2022
Closest in time.
Adaptive temporal-frequency network for time-series forecasting
Zhangjing Yang, Weiwu Yan, Xiaolin Huang, and Lin Mei · 2022
Closest in time.
FEDformer: Frequency enhanced decomposed transformer for long-term series forecasting
Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin · 2022
Closest in time.