Fetching the paper…
Reading the bibliography…
Time series forecasting is a critical and challenging task in practical application.
Time-series data mining
Philippe Esling and Carlos Agon. 2012 · 2012
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015 · 2015
Earlier work this paper cites.
Multi-scale convolutional neural networks for time series classification
Zhicheng Cui, Wenlin Chen, and Yixin Chen. 2016 · 2016
Earlier work this paper cites.
Compress: Self-supervised learning by compressing representations
Soroush Abbasi Koohpayegani, Ajinkya Tejankar, and Hamed Pirsiavash. 2020 · 2020
Earlier work this paper cites.
Bootstrap your own latent-a new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al · 2020
Earlier work this paper cites.
Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 9729–9738
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. 2020 · 2020
Earlier work this paper cites.
Adarnn: Adaptive learning and forecasting of time series. In Proceedings of the 30th ACM international conference on information & knowledge management . 402–411
Yuntao Du, Jindong Wang, Wenjie Feng, Sinno Pan, Tao Qin, Renjun Xu, and Chongjun Wang. 2021 · 2021
Earlier work this paper cites.
Self-knowledge distillation with progressive refinement of targets. In Proceedings of the IEEE/CVF international conference on computer vision . 6567–6576
Kyungyul Kim, ByeongMoon Ji, Doyoung Yoon, and Sangheum Hwang. 2021 · 2021
Earlier work this paper cites.
Time-series forecasting with deep learning: a survey
Bryan Lim and Stefan Zohren. 2021 · 2021
Earlier work this paper cites.
Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting
Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long. 2021 · 2021
Earlier work this paper cites.
A transformer-based framework for multivariate time series representation learning. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining . 2114–2124
George Zerveas, Srideepika Jayaraman, Dhaval Patel, Anuradha Bhamidipaty, and Carsten Eickhoff. 2021 · 2021
Earlier work this paper cites.
Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence , Vol. 35. 11106–11115
Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021 · 2021
Earlier work this paper cites.
Data2vec: A general framework for self-supervised learning in speech, vision and language. In International Conference on Machine Learning . PMLR, 1298–1312
Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, and Michael Auli. 2022 · 2022
Earlier work this paper cites.
Deep learning for time series forecasting: Tutorial and literature survey
Konstantinos Benidis, Syama Sundar Rangapuram, Valentin Flunkert, Yuyang Wang, Danielle Maddix, Caner Turkmen, Jan Gasthaus, Michael Bohlke-Schneider, David Salinas, Lorenzo Stella, et al · 2022
Earlier work this paper cites.
Self-supervised representation learning: Introduction, advances, and challenges
Linus Ericsson, Henry Gouk, Chen Change Loy, and Timothy M Hospedales. 2022 · 2022
Earlier work this paper cites.
Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 16000–16009
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. 2022 · 2022
Earlier work this paper 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
Cited alongside, same era.
Scaleformer: iterative multi-scale refining transformers for time series forecasting
Amin Shabani, Amir Abdi, Lili Meng, and Tristan Sylvain. 2022 · 2022
Cited alongside, same era.
Pre-training enhanced spatial-temporal graph neural network for multivariate time series forecasting. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 1567–1577
Zezhi Shao, Zhao Zhang, Fei Wang, and Yongjun Xu. 2022 · 2022
Cited alongside, same era.
Learning latent seasonal-trend representations for time series forecasting
Zhiyuan Wang, Xovee Xu, Weifeng Zhang, Goce Trajcevski, Ting Zhong, and Fan Zhou. 2022b · 2022
Cited alongside, same era.
TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked Autoencoders
Mingyue Cheng, Qi Liu, Zhiding Liu, Hao Zhang, Rujiao Zhang, and Enhong Chen. 2023 · 2023
Later among the works it cites.
SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling
Jiaxiang Dong, Haixu Wu, Haoran Zhang, Li Zhang, Jianmin Wang, and Mingsheng Long. 2023b · 2023
Later among the works it cites.
Label-efficient time series representation learning: A review
Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee-Keong Kwoh, and Xiaoli Li. 2023 · 2023
Later among the works it cites.
Large models for time series and spatio-temporal data: A survey and outlook
Ming Jin, Qingsong Wen, Yuxuan Liang, Chaoli Zhang, Siqiao Xue, Xue Wang, James Zhang, Yi Wang, Haifeng Chen, Xiaoli Li, et al · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Qingsong Wen, Tian Zhou, Chaoli Zhang, Weiqi Chen, Ziqing Ma, Junchi Yan, and Liang Sun. 2022 · 2022
Cited alongside, same era.
Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, and Steven Hoi. 2022 · 2022
Cited alongside, same era.
TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis. In The Eleventh International Conference on Learning Representations
Haixu Wu, Tengge Hu, Yong Liu, Hang Zhou, Jianmin Wang, and Mingsheng Long. 2022 · 2022
Cited alongside, same era.
Contrastive adversarial knowledge distillation for deep model compression in time-series regression tasks
Qing Xu, Zhenghua Chen, Mohamed Ragab, Chao Wang, Min Wu, and Xiaoli Li. 2022 · 2022
Cited alongside, same era.
Ts2vec: Towards universal representation of time series. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 36. 8980–8987
Zhihan Yue, Yujing Wang, Juanyong Duan, Tianmeng Yang, Congrui Huang, Yunhai Tong, and Bixiong Xu. 2022 · 2022
Cited alongside, same era.
Self-supervised contrastive pre-training for time series via time-frequency consistency
Xiang Zhang, Ziyuan Zhao, Theodoros Tsiligkaridis, and Marinka Zitnik. 2022 · 2022
Cited alongside, same era.
Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting. In The Eleventh International Conference on Learning Representations
Yunhao Zhang and Junchi Yan. 2022 · 2022
Cited alongside, same era.
Two-phased federated learning with clustering and personalization for natural gas load forecasting. In International Workshop on Trustworthy Federated Learning . Springer, 130–143
Shubao Zhao, Jia Liu, Guoliang Ma, Jie Yang, Di Liu, and Zengxiang Li. 2022 · 2022
Cited alongside, same era.
Zhe Li, Zhongwen Rao, Lujia Pan, Pengyun Wang, and Zenglin Xu. 2023c · 2023
Later among the works it cites.
Mts-mixers: Multivariate time series forecasting via factorized temporal and channel mixing
Zhe Li, Zhongwen Rao, Lujia Pan, and Zenglin Xu. 2023b · 2023
Later among the works it cites.
Frequency-Aware Masked Autoencoders for Multimodal Pretraining on Biosignals
Ran Liu, Ellen L Zippi, Hadi Pouransari, Chris Sandino, Jingping Nie, Hanlin Goh, Erdrin Azemi, and Ali Moin. 2023 · 2023
Later among the works it cites.
A Survey on Time-Series Pre-Trained Models
Qianli Ma, Zhen Liu, Zhenjing Zheng, Ziyang Huang, Siying Zhu, Zhongzhong Yu, and James T Kwok. 2023 · 2023
Later among the works it cites.
MHCCL: masked hierarchical cluster-wise contrastive learning for multivariate time series. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 37. 9153–9161
Qianwen Meng, Hangwei Qian, Yong Liu, Lizhen Cui, Yonghui Xu, and Zhiqi Shen. 2023 · 2023
Later among the works it cites.
Multi-mode online knowledge distillation for self-supervised visual representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 11848–11857
Kaiyou Song, Jin Xie, Shan Zhang, and Zimeng Luo. 2023 · 2023
Later among the works it cites.
Transformers in time series: A survey. In International Joint Conference on Artificial Intelligence (IJCAI)
Qingsong Wen, Tian Zhou, Chaoli Zhang, Weiqi Chen, Ziqing Ma, Junchi Yan, and Liang Sun. 2023 · 2023
Later among the works it cites.
Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence , Vol. 37. 11121–11128
Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023 · 2023
Later among the works it cites.
Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects
Kexin Zhang, Qingsong Wen, Chaoli Zhang, Rongyao Cai, Ming Jin, Yong Liu, James Zhang, Yuxuan Liang, Guansong Pang, Dongjin Song, et al · 2023
Later among the works it cites.
Multi-resolution Time-Series Transformer for Long-term Forecasting
Yitian Zhang, Liheng Ma, Soumyasundar Pal, Yingxue Zhang, and Mark Coates. 2023a · 2023
Later among the works it cites.
SimTS: Rethinking Contrastive Representation Learning for Time Series Forecasting
Xiaochen Zheng, Xingyu Chen, Manuel Schürch, Amina Mollaysa, Ahmed Allam, and Michael Krauthammer. 2023 · 2023
Later among the works it cites.
A multi-scale decomposition mlp-mixer for time series analysis
Shuhan Zhong, Sizhe Song, Guanyao Li, Weipeng Zhuo, Yang Liu, and S-H Gary Chan. 2023 · 2023
Later among the works it cites.