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Forecasting multivariate time series data, which involves predicting future values of variables over time using historical data, has significant practical applications.
Graph wavenet for deep spatial-temporal graph modeling. In Proceedings of the 28th International Joint Conference on Artificial Intelligence . 1907–1913
Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019 · 1913
Earlier work this paper cites.
Investigating causal relations by econometric models and cross-spectral methods
Clive WJ Granger. 1969a · 1969
Earlier work this paper cites.
Investigating causal relations by econometric models and cross-spectral methods
Clive WJ Granger. 1969b · 1969
Earlier work this paper cites.
Optimal choice of AR and MA parts in autoregressive moving average models
Rangasami L Kashyap. 1982 · 1982
Earlier work this paper cites.
Granger causality and the times series analysis of political relationships
John R Freeman. 1983 · 1983
Earlier work this paper cites.
Short term traffic forecasting using time series methods
CK Moorthy and BG Ratcliffe. 1988 · 1988
Earlier work this paper cites.
A comparison of fuzzy forecasting and Markov modeling
Joe Sullivan and William H Woodall. 1994 · 1994
Earlier work this paper cites.
Analysis of an adaptive time-series autoregressive moving-average (ARMA) model for short-term load forecasting
Jiann-Fuh Chen, Wei-Ming Wang, and Chao-Ming Huang. 1995 · 1995
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997a · 1997
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997b · 1997
Earlier work this paper cites.
The use of ARIMA models for reliability forecasting and analysis
Siu Lau Ho and Min Xie. 1998 · 1998
Earlier work this paper cites.
Advanced spectral methods for climatic time series
Michael Ghil, MR Allen, MD Dettinger, K Ide, D Kondrashov, ME Mann, Andrew W Robertson, A Saunders, Y Tian, Fa Varadi, et al · 2002
Earlier work this paper cites.
Financial time series forecasting using support vector machines
Kyoung-jae Kim. 2003 · 2003
Earlier work this paper cites.
Analyzing multiple nonlinear time series with extended Granger causality
Yonghong Chen, Govindan Rangarajan, Jianfeng Feng, and Mingzhou Ding. 2004 · 2004
Earlier work this paper cites.
A PCA-based similarity measure for multivariate time series. In Proceedings of the 2nd ACM international workshop on Multimedia databases . 65–74
Kiyoung Yang and Cyrus Shahabi. 2004 · 2004
Earlier work this paper cites.
A linear non-Gaussian acyclic model for causal discovery
Shohei Shimizu, Patrik O Hoyer, Aapo Hyvärinen, Antti Kerminen, and Michael Jordan. 2006 · 2006
Earlier work this paper cites.
Hunting Causes and Using Them: Approaches in Philosophy and Economics
Nancy Cartwright. 2007 · 2007
Earlier work this paper cites.
Granger causality and path diagrams for multivariate time series
Michael Eichler. 2007 · 2007
Earlier work this paper cites.
Causality
J Pearl. 2009 · 2009
Cited alongside, same era.
Gaussian mixture models
Douglas A Reynolds et al · 2009
Cited alongside, same era.
Estimation of a structural vector autoregression model using non-gaussianity
Aapo Hyvärinen, Kun Zhang, Shohei Shimizu, and Patrik O Hoyer. 2010 · 2010
Cited alongside, same era.
Financial time series forecasting with machine learning techniques: a survey.. In ESANN
Bjoern Krollner, Bruce J Vanstone, Gavin R Finnie, et al · 2010
Cited alongside, same era.
Granger causality
Gebhard Kirchgässner, Jürgen Wolters, Uwe Hassler, Gebhard Kirchgässner, Jürgen Wolters, and Uwe Hassler. 2013 · 2013
Cited alongside, same era.
An overview of health forecasting
Ireneous N Soyiri and Daniel D Reidpath. 2013 · 2013
Cited alongside, same era.
Deep state space models for time series forecasting
Syama Sundar Rangapuram, Matthias W Seeger, Jan Gasthaus, Lorenzo Stella, Yuyang Wang, and Tim Januschowski. 2018 · 2018
Later among the works it cites.
A comparison of ARIMA and LSTM in forecasting time series. In 2018 17th IEEE international conference on machine learning and applications (ICMLA) . IEEE, 1394–1401
Sima Siami-Namini, Neda Tavakoli, and Akbar Siami Namin. 2018 · 2018
Later among the works it cites.
Stock price forecasting model based on modified convolution neural network and financial time series analysis
Jiasheng Cao and Jinghan Wang. 2019 · 2019
Later among the works it cites.
Causal discovery and forecasting in nonstationary environments with state-space models. In International conference on machine learning . PMLR, 2901–2910
Biwei Huang, Kun Zhang, Mingming Gong, and Clark Glymour. 2019 · 2019
Later among the works it cites.
EA-LSTM: Evolutionary attention-based LSTM for time series prediction
Youru Li, Zhenfeng Zhu, Deqiang Kong, Hua Han, and Yao Zhao. 2019 · 2019
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Dynamic covariance models for multivariate financial time series. In International Conference on Machine Learning . PMLR, 558–566
Yue Wu, José Miguel Hernández-Lobato, and Ghahramani Zoubin. 2013 · 2013
Cited alongside, same era.
Study of effectiveness of time series modeling (ARIMA) in forecasting stock prices
Prapanna Mondal, Labani Shit, and Saptarsi Goswami. 2014 · 2014
Cited alongside, same era.
Long short-term memory recurrent neural network architectures for large scale acoustic modeling
Hasim Sak, Andrew W Senior, and Françoise Beaufays. 2014 · 2014
Cited alongside, same era.
Time series analysis of water quality parameters at Stillaguamish River using order series method
Farid Khalil Arya and Lan Zhang. 2015 · 2015
Cited alongside, same era.
Time series analysis: forecasting and control
George EP Box, Gwilym M Jenkins, Gregory C Reinsel, and Greta M Ljung. 2015 · 2015
Cited alongside, same era.
A survey on data mining techniques applied to electricity-related time series forecasting
Francisco Martínez-Álvarez, Alicia Troncoso, Gualberto Asencio-Cortés, and José C Riquelme. 2015 · 2015
Cited alongside, same era.
Later among the works it cites.
Deep factors for forecasting. In International conference on machine learning . PMLR, 6607–6617
Yuyang Wang, Alex Smola, Danielle Maddix, Jan Gasthaus, Dean Foster, and Tim Januschowski. 2019 · 2019
Later among the works it cites.
A spatio-temporal attention-based spot-forecasting framework for urban traffic prediction
Rodrigo de Medrano and Jose L Aznarte. 2020 · 2020
Later among the works it cites.
Multivariate time series forecasting via attention-based encoder–decoder framework
Shengdong Du, Tianrui Li, Yan Yang, and Shi-Jinn Horng. 2020 · 2020
Later among the works it cites.
AI in healthcare: time-series forecasting using statistical, neural, and ensemble architectures
Shruti Kaushik, Abhinav Choudhury, Pankaj Kumar Sheron, Nataraj Dasgupta, Sayee Natarajan, Larry A Pickett, and Varun Dutt. 2020 · 2020
Later among the works it cites.
DeepAR: Probabilistic forecasting with autoregressive recurrent networks
David Salinas, Valentin Flunkert, Jan Gasthaus, and Tim Januschowski. 2020 · 2020
Later among the works it cites.
Connecting the dots: Multivariate time series forecasting with graph neural networks. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining . 753–763
Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Xiaojun Chang, and Chengqi Zhang. 2020 · 2020
Later among the works it cites.
Haoyan Xu, Yida Huang, Ziheng Duan, Jie Feng, and Pengyu Song. 2020 · 2020
Later among the works it cites.
Graph neural networks: A review of methods and applications
Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun. 2020 · 2020
Later among the works it cites.
Recurrent neural networks for time series forecasting: Current status and future directions
Hansika Hewamalage, Christoph Bergmeir, and Kasun Bandara. 2021 · 2021
Later among the works it cites.
Discrete Graph Structure Learning for Forecasting Multiple Time Series. In International Conference on Learning Representations
Chao Shang, Jie Chen, and Jinbo Bi. 2021 · 2021
Later among the works it cites.
Financial time series forecasting with multi-modality graph neural network
Dawei Cheng, Fangzhou Yang, Sheng Xiang, and Jin Liu. 2022 · 2022
Later among the works it cites.
Towards spatio-temporal aware traffic time series forecasting. In 2022 IEEE 38th International Conference on Data Engineering (ICDE) . IEEE, 2900–2913
Razvan-Gabriel Cirstea, Bin Yang, Chenjuan Guo, Tung Kieu, and Shirui Pan. 2022 · 2022
Later among the works it cites.
Sparsification and Filtering for Spatial-temporal GNN in Multivariate Time-series
Yuanrong Wang and Tomaso Aste. 2022 · 2022
Later among the works it cites.