Fetching the paper…
Reading the bibliography…
Multivariate time series data comprises various channels of variables.
Probabilistic forecasting with spline quantile function RNNs. In AISTATS . PMLR, 1901–1910
Jan Gasthaus, Konstantinos Benidis, Yuyang Wang, Syama Sundar Rangapuram, David Salinas, Valentin Flunkert, and Tim Januschowski. 2019 · 1910
Earlier work this paper cites.
Robust Loss Functions under Label Noise for Deep Neural Networks. In AAAI , Satinder Singh and Shaul Markovitch (Eds.). 1919–1925
Aritra Ghosh, Himanshu Kumar, and P. S. Sastry. 2017 · 1925
Earlier work this paper cites.
On a method of investigating periodicities in disturbed series, with special reference to Wolfer’s sunspot numbers
George Udny Yule. 1927 · 1927
Earlier work this paper cites.
On periodicity in series of related terms
Gilbert Thomas Walker. 1931 · 1931
Earlier work this paper cites.
The spectral analysis of time-series
Gwilym M Jenkins and MB Priestley. 1957 · 1957
Earlier work this paper cites.
Time-Series. 2nd edn
OD Anderson. 1976 · 1976
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
Time series analysis
Henrik Madsen. 2007 · 2007
Earlier work this paper cites.
Sparse and low-rank matrix decomposition via alternating direction methods
Xiaoming Yuan and Junfeng Yang. 2009 · 2009
Earlier work this paper cites.
Robust Subspace Segmentation by Low-Rank Representation. In ICML , Johannes Fürnkranz and Thorsten Joachims (Eds.). Omnipress, 663–670
Guangcan Liu, Zhouchen Lin, and Yong Yu. 2010 · 2010
Earlier work this paper cites.
On the Properties of Neural Machine Translation: Encoder-Decoder Approaches. In SSST@EMNLP , Dekai Wu, Marine Carpuat, Xavier Carreras, and Eva Maria Vecchi (Eds.). Association for Computational Linguistics, 103–111
Kyunghyun Cho, Bart van Merrienboer, Dzmitry Bahdanau, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Time series analysis: forecasting and control
George EP Box, Gwilym M Jenkins, Gregory C Reinsel, and Greta M Ljung. 2015 · 2015
Earlier work this paper cites.
Making risk minimization tolerant to label noise
Aritra Ghosh, Naresh Manwani, and P. S. Sastry. 2015 · 2015
Earlier work this paper cites.
Xgboost: A scalable tree boosting system. In SIGKDD . 785–794
Tianqi Chen and Carlos Guestrin. 2016 · 2016
Earlier work this paper cites.
Time-series extreme event forecasting with neural networks at uber. In ICML , Vol. 34. 1–5
Nikolay Laptev, Jason Yosinski, Li Erran Li, and Slawek Smyl. 2017 · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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 · 2018
Cited alongside, same era.
Correlated time series forecasting using multi-task deep neural networks. In ICKM . 1527–1530
Razvan-Gabriel Cirstea, Darius-Valer Micu, Gabriel-Marcel Muresan, Chenjuan Guo, and Bin Yang. 2018 · 2018
Cited alongside, same era.
Speech-Transformer: A No-Recurrence Sequence-to-Sequence Model for Speech Recognition. In ICASSP . IEEE, 5884–5888
Linhao Dong, Shuang Xu, and Bo Xu. 2018 · 2018
Cited alongside, same era.
Forecasting: principles and practice
Rob J Hyndman and George Athanasopoulos. 2018 · 2018
Cited alongside, same era.
DeepAR: Probabilistic forecasting with autoregressive recurrent networks
David Salinas, Valentin Flunkert, Jan Gasthaus, and Tim Januschowski. 2020 · 2020
Later among the works it cites.
A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting
Slawek Smyl. 2020 · 2020
Later among the works it cites.
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks. In KDD ’20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, CA, USA, August 23-27, 2020 , Rajesh Gupta, Yan Liu, Jiliang Tang, and B. Aditya Prakash (Eds.). ACM, 753–763
Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Xiaojun Chang, and Chengqi Zhang. 2020 · 2020
Later among the works it cites.
METRO: A Generic Graph Neural Network Framework for Multivariate Time Series Forecasting
Yue Cui, Kai Zheng, Dingshan Cui, Jiandong Xie, Liwei Deng, Feiteng Huang, and Xiaofang Zhou. 2021 · 2021
Later among the works it cites.
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In ICLR
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Modeling long-and short-term temporal patterns with deep neural networks. In SIGIR . 95–104
Guokun Lai, Wei-Cheng Chang, Yiming Yang, and Hanxiao Liu. 2018 · 2018
Cited alongside, same era.
Foundations of machine learning
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar. 2018 · 2018
Cited alongside, same era.
Deep State Space Models for Time Series Forecasting. In NeurIPS , Samy Bengio, Hanna M. Wallach, Hugo Larochelle, Kristen Grauman, Nicolò Cesa-Bianchi, and Roman Garnett (Eds.). 7796–7805
Syama Sundar Rangapuram, Matthias W. Seeger, Jan Gasthaus, Lorenzo Stella, Yuyang Wang, and Tim Januschowski. 2018 · 2018
Cited alongside, same era.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), Minneapolis, MN, USA, June 2-7, 2019 . 4171–4186
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Unsupervised Scalable Representation Learning for Multivariate Time Series. In NeurIPS , Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d’Alché-Buc, Emily B. Fox, and Roman Garnett (Eds.). 4652–4663
Jean-Yves Franceschi, Aymeric Dieuleveut, and Martin Jaggi. 2019 · 2019
Cited alongside, same era.
N-BEATS: Neural basis expansion analysis for interpretable time series forecasting. In ICLR
Boris N Oreshkin, Dmitri Carpov, Nicolas Chapados, and Yoshua Bengio. 2019 · 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 · 2019
Cited alongside, same era.
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. 2021 · 2021
Later among the works it cites.
Do We Really Need Deep Learning Models for Time Series Forecasting?
Shereen Elsayed, Daniela Thyssens, Ahmed Rashed, Lars Schmidt-Thieme, and Hadi Samer Jomaa. 2021 · 2021
Later among the works it cites.
Principles and algorithms for forecasting groups of time series: Locality and globality
Pablo Montero-Manso and Rob J Hyndman. 2021 · 2021
Later among the works it cites.
AutoCTS: Automated Correlated Time Series Forecasting
Xinle Wu, Dalin Zhang, Chenjuan Guo, Chaoyang He, Bin Yang, and Christian S. Jensen. 2021b · 2021
Later among the works it cites.
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting. In AAAI , Vol. 35. 11106–11115
Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021 · 2021
Later among the works it cites.
LoRA: Low-Rank Adaptation of Large Language Models. In ICLR
Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022 · 2022
Later among the works it cites.
Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting. In ICLR
Shizhan Liu, Hang Yu, Cong Liao, Jianguo Li, Weiyao Lin, Alex X Liu, and Schahram Dustdar. 2022 · 2022
Later among the works it cites.
A Time Series is Worth 64 Words: Long-term Forecasting with Transformers
Yuqi Nie, Nam H. Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam. 2022 · 2022
Later among the works it cites.
Are Transformers Effective for Time Series Forecasting?
Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2022 · 2022
Later among the works it cites.
FEDformer: Frequency enhanced decomposed transformer for long-term series forecasting. In ICML
Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022 · 2022
Later among the works it cites.