Fetching the paper…
Reading the bibliography…
The performance of time series forecasting has recently been greatly improved by the introduction of transformers.
Studies in the history of probability and statistics. where shall the history of statistics begin?
M. G. Kendall · 1960
Earlier work this paper cites.
Robust Estimation of a Location Parameter
Peter J. Huber · 1964
Earlier work this paper cites.
Some recent advances in forecasting and control
George EP Box and Gwilym M Jenkins · 1968
Earlier work this paper cites.
Oscillation and chaos in physiological control systems
Michael C Mackey and Leon Glass · 1977
Earlier work this paper cites.
Studies in astronomical time series analysis. i-modeling random processes in the time domain
Jeffery D Scargle · 1981
Earlier work this paper cites.
The dimension of chaotic attractors
J.Doyne Farmer, Edward Ott, and James A. Yorke · 1983
Earlier work this paper cites.
Induction of multiscale temporal structure
Michael C Mozer · 1991
Earlier work this paper cites.
What is a good forecast? an essay on the nature of goodness in weather forecasting
Allan H Murphy · 1993
Earlier work this paper cites.
Improving predictive inference under covariate shift by weighting the log-likelihood function
Hidetoshi Shimodaira · 2000
Earlier work this paper cites.
Multi-scale and hidden resolution time series models
Marco AR Ferreira, David M Higdon, Herbert KH Lee, and Mike West · 2006
Earlier work this paper cites.
Forecasting with exponential smoothing: the state space approach
Rob Hyndman, Anne B Koehler, J Keith Ord, and Ralph D Snyder · 2008
Earlier work this paper cites.
Time series: theory and methods
Peter J Brockwell and Richard A Davis · 2009
Earlier work this paper cites.
Forecasting for inventory planning: a 50-year review
Aris A Syntetos, John E Boylan, and Stephen M Disney · 2009
Earlier work this paper cites.
Financial time series forecasting with machine learning techniques: a survey
Bjoern Krollner, Bruce J Vanstone, Gavin R Finnie, et al · 2010
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Earlier work this paper cites.
Hierarchical multiscale recurrent neural networks
Junyoung Chung, Sungjin Ahn, and Yoshua Bengio · 2016
Earlier work this paper cites.
Multi-scale convolutional neural networks for time series classification
Zhicheng Cui, Wenlin Chen, and Yixin Chen · 2016
Cited alongside, same era.
Mackey-glass noisy chaotic time series prediction by a swarm-optimized neural network
CH López-Caraballo, I Salfate, JA Lazzús, P Rojas, M Rivera, and L Palma-Chilla · 2016
Cited alongside, same era.
An empirical evaluation of generic convolutional and recurrent networks for sequence modeling
Shaojie Bai, J. Zico Kolter, and Vladlen Koltun · 2018
Cited alongside, same era.
Hierarchical deep generative models for multi-rate multivariate time series
Zhengping Che, Sanjay Purushotham, Guangyu Li, Bo Jiang, and Yan Liu · 2018
Cited alongside, same era.
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.
Multi-scale transformer language models
Sandeep Subramanian, Ronan Collobert, Marc’Aurelio Ranzato, and Y-Lan Boureau · 2020
Later among the works it cites.
Deep transformer models for time series forecasting: The influenza prevalence case
Neo Wu, Bradley Green, Xue Ben, and Shawn O’Banion · 2020
Later among the works it cites.
Time-aware multi-scale rnns for time series modeling
Zipeng Chen, Qianli Ma, and Zhenxi Lin · 2021
Later among the works it cites.
Multiscale vision transformers
Haoqi Fan, Bo Xiong, Karttikeya Mangalam, Yanghao Li, Zhicheng Yan, Jitendra Malik, and Christoph Feichtenhofer · 2021
Later among the works it cites.
Yformer: U-net inspired transformer architecture for far horizon time series forecasting
Kiran Madhusudhanan, Johannes Burchert, Nghia Duong-Trung, Stefan Born, and Lars Schmidt-Thieme · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Multi-level wavelet-cnn for image restoration
Pengju Liu, Hongzhi Zhang, Kai Zhang, Liang Lin, and Wangmeng Zuo · 2018
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
Cited alongside, same era.
A general and adaptive robust loss function
Jonathan T Barron · 2019
Cited alongside, same era.
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
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 Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
Cited alongside, same era.
FEDformer: Frequency enhanced decomposed transformer for long-term series forecasting
Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin · 2019
Cited alongside, same era.
Rethinking attention with performers, 2020
Krzysztof Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Andreea Gane, Tamas Sarlos, Peter Hawkins, Jared Davis, Afroz Mohiuddin, Lukasz Kaiser, David Belanger, Lucy Colwell, and Adrian Weller · 2020
Cited alongside, same era.
Later among the works it cites.
Hierarchical transformers are more efficient language models
Piotr Nawrot, Szymon Tworkowski, Michał Tyrolski, Łukasz Kaiser, Yuhuai Wu, Christian Szegedy, and Henryk Michalewski · 2021
Later among the works it cites.
Probabilistic transformer for time series analysis
Binh Tang and David S Matteson · 2021
Later among the works it cites.
Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting
Jiehui Xu, Jianmin Wang, Mingsheng Long, et al · 2021
Later among the works it cites.
A transformer-based framework for multivariate time series representation learning
George Zerveas, Srideepika Jayaraman, Dhaval Patel, Anuradha Bhamidipaty, and Carsten Eickhoff · 2021
Later among the works it cites.
Multi-scale vision longformer: A new vision transformer for high-resolution image encoding
Pengchuan Zhang, Xiyang Dai, Jianwei Yang, Bin Xiao, Lu Yuan, Lei Zhang, and Jianfeng Gao · 2021
Later among the works it cites.
Multi-scale group transformer for long sequence modeling in speech separation
Yucheng Zhao, Chong Luo, Zheng-Jun Zha, and Wenjun Zeng · 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.
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
Closest in time.
Multi-scale adaptive graph neural network for multivariate time series forecasting
Ling Chen, Donghui Chen, Zongjiang Shang, Youdong Zhang, Bo Wen, and Chenghu Yang · 2022
Closest in time.
Dazhao Du, Bing Su, and Zhewei Wei · 2022
Closest in time.