Fetching the paper…
Reading the bibliography…
Time series forecasting has played the key role in different industrial, including finance, traffic, energy, and healthcare domains.
Empirical orthogonal functions and statistical weather prediction
Edward N Lorenz · 1956
Earlier work this paper cites.
Forecasting trends and seasonal by exponentially weighted moving averages
Charles C Holt · 1957
Earlier work this paper cites.
Prediction and regulation by linear least-square methods
Peter Whittle · 1963
Earlier work this paper cites.
Market and industry factors in stock price behavior
Benjamin F King · 1966
Earlier work this paper cites.
Vector autoregressions and cointegration
Mark W. Watson · 1993
Earlier work this paper cites.
The discrete Fourier transform: theory, algorithms and applications
Duraisamy Sundararajan · 2001
Earlier work this paper cites.
Arima models and the box–jenkins methodology
Dimitros Asteriou and Stephen G Hall · 2011
Earlier work this paper cites.
Efficient estimation of word representations in vector space
Tomás Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean · 2013
Earlier work this paper cites.
Stock price prediction using the arima model
Adebiyi A Ariyo, Adewumi O Adewumi, and Charles K Ayo · 2014
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.
Stock price prediction via discovering multi-frequency trading patterns
Liheng Zhang, Charu C. Aggarwal, and Guo-Jun Qi · 2017
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
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.
Diffusion convolutional recurrent neural network: Data-driven traffic forecasting
Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu · 2018
Earlier work this paper cites.
Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting
Bing Yu, Haoteng Yin, and Zhanxing Zhu · 2018
Earlier work this paper cites.
N-beats: Neural basis expansion analysis for interpretable time series forecasting
Boris N Oreshkin, Dmitri Carpov, Nicolas Chapados, and Yoshua Bengio · 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.
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.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Z. Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
Cited alongside, same era.
Deepar: Probabilistic forecasting with autoregressive recurrent networks
David Salinas, Valentin Flunkert, Jan Gasthaus, and Tim Januschowski · 2020
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
Later among the works it cites.
Scinet: time series modeling and forecasting with sample convolution and interaction
Minhao Liu, Ailing Zeng, Muxi Chen, Zhijian Xu, Qiuxia Lai, Lingna Ma, and Qiang Xu · 2022
Later among the works it cites.
Less is more: Fast multivariate time series forecasting with light sampling-oriented mlp structures
Tianping Zhang, Yizhuo Zhang, Wei Cao, Jiang Bian, Xiaohan Yi, Shun Zheng, and Jian Li · 2022
Later among the works it cites.
Are transformers effective for time series forecasting?
Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu · 2022
Later among the works it cites.
FEDformer: Frequency enhanced decomposed transformer for long-term series forecasting
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
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
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.
Adaptive graph convolutional recurrent network for traffic forecasting
Lei Bai, Lina Yao, Can Li, Xianzhi Wang, and Can Wang · 2020
Cited alongside, same era.
Reformer: The efficient transformer
Nikita Kitaev, Lukasz Kaiser, and Anselm Levskaya · 2020
Cited alongside, same era.
Deep learning for time series forecasting: A survey
José Torres, Dalil Hadjout, Abderrazak Sebaa, Francisco Martínez-Álvarez, and Alicia Troncoso · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting
Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long · 2021
Cited alongside, same era.
Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin · 2022
Later among the works it cites.
Cost: Contrastive learning of disentangled seasonal-trend representations for time series forecasting
Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, and Steven C. H. Hoi · 2022
Later among the works it cites.
Film: Frequency improved legendre memory model for long-term time series forecasting
Tian Zhou, Ziqing Ma, Xue Wang, Qingsong Wen, Liang Sun, Tao Yao, Wotao Yin, and Rong Jin · 2022
Later among the works it cites.
DEPTS: deep expansion learning for periodic time series forecasting
Wei Fan, Shun Zheng, Xiaohan Yi, Wei Cao, Yanjie Fu, Jiang Bian, and Tie-Yan Liu · 2022
Later among the works it cites.
N-hits: Neural hierarchical interpolation for time series forecasting
Cristian Challu, Kin G. Olivares, Boris N. Oreshkin, Federico Garza, Max Mergenthaler, and Artur Dubrawski · 2022
Later among the works it cites.
Dish-ts: A general paradigm for alleviating distribution shift in time series forecasting
Wei Fan, Pengyang Wang, Dongkun Wang, Dongjie Wang, Yuanchun Zhou, and Yanjie Fu · 2023
Closest in time.
Learning informative representation for fairness-aware multivariate time-series forecasting: A group-based perspective
Hui He, Qi Zhang, Shoujin Wang, Kun Yi, Zhendong Niu, and Longbing Cao · 2023
Closest in time.
A survey on deep learning based time series analysis with frequency transformation
Kun Yi, Qi Zhang, Longbing Cao, Shoujin Wang, Guodong Long, Liang Hu, Hui He, Zhendong Niu, Wei Fan, and Hui Xiong · 2023
Closest in time.
Are transformers effective for time series forecasting?
Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu · 2023
Closest in time.
A time series is worth 64 words: Long-term forecasting with transformers
Yuqi Nie, Nam H. Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam · 2023
Closest in time.