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Time series foundation models have shown impressive performance on a variety of tasks, across a wide range of domains, even in zero-shot settings.
N-BEATS: neural basis expansion analysis for interpretable time series forecasting
Boris N. Oreshkin, Dmitri Carpov, Nicolas Chapados, and Yoshua Bengio · 1905
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The UCR time series classification archive, July 2015
Yanping Chen, Eamonn Keogh, Bing Hu, Nurjahan Begum, Anthony Bagnall, Abdullah Mueen, and Gustavo Batista · 2015
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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RoBERTa: A robustly optimized BERT pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, et al · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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Language models are few-shot learners
Tom B Brown, Benjamin Mann, Nick Ryder, et al · 2020
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Transformers are RNNs: Fast autoregressive transformers with linear attention
Angelos Katharopoulos, Apoorv Vyas, Nikolaos Pappas, and François Fleuret · 2020
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Exploring the limits of transfer learning with a Unified Text-to-Text Transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu · 2020
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Informer: Beyond efficient transformer for long sequence time-series forecasting
Haixu Zhou, Shanghang Zhang, Jie Peng, Shuai Zhang, Guangjian Li, Hanzhang Xiong, Wancai Zhang, Tien-Ju Lin, Xiaolong Chu, Jingren Zhang, et al · 2021
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Scale efficiently: Insights from pre-training and fine-tuning transformers, 2022
Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, and Donald Metzler · 2022
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A decoder-only foundation model for time-series forecasting
Abhimanyu Das, Weihao Kong, Rajat Sen, and Yichen Zhou · 2023
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TimeGPT-1, 2023
Azul Garza and Max Mergenthaler-Canseco · 2023
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Time-LLM: Time series forecasting by reprogramming large language models
Ming Jin, Shuang Wang, Li Ma, Zhen Chu, Jia-Yu Zhang, Xiong Shi, Pei-Yuan Chen, Yanzhi Liang, Yufeng Li, Sinno Pan, and Qian Wen · 2023
Chronos: Learning the language of time series
Abdul Fatir Ansari, Lorenzo Stella, Caner Turkmen, Xiyuan Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama Syndar Rangapuram, Sebastian Pineda Arango, Shubham Kapoor, Jasper Zschiegner, Danielle C. Maddix, Hao Wang, Michael W. Mahoney, Kari Torkkola, Andrew Gordon Wilson, Michael Bohlke-Schneider, and Yuyang Wang · 2024
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Vijay Ekambaram, Arindam Jati, Nam H Nguyen, Pankaj Dayama, Chandra Reddy, Wesley M Gifford, and Jayant Kalagnanam · 2024
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MOMENT: A family of open time-series foundation models
Mononito Goswami, Konrad Szafer, Arjun Choudhry, Yifu Cai, Shuo Li, and Artur Dubrawski · 2024
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iTransformer: Inverted transformers are effective for time series forecasting
Yong Liu, Tengge Hu, Haoran Zhang, Haixu Wu, Shiyu Wang, Lintao Ma, and Mingsheng Long · 2024
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Leave no context behind: Efficient infinite context transformers with infini-attention
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A time series is worth 64 words: Long-term forecasting with transformers
Yuqi Nie, Nam H. Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam · 2023
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Lag-Llama: Towards foundation models for probabilistic time series forecasting
Kashif Rasul, Arjun Ashok, Andrew Robert Williams, Arian Khorasani, George Adamopoulos, Rishika Bhagwatkar, Marin Biloš, Hena Ghonia, Nadhir Vincent Hassen, Anderson Schneider, et al · 2023
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Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting
Yunhao Zhang and Junchi Yan · 2023
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Timer: Generative pre-trained transformers are large time series models
Yong Liu, Haoran Zhang, Chenyu Li, Xiangdong Huang, Jianmin Wang, and Mingsheng Long
Cited in the paper.
Tsendsuren Munkhdalai, Manaal Faruqui, and Siddharth Gopal · 2024
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Unified training of universal time series forecasting transformers
Gerald Woo, Chenghao Liu, Akshat Kumar, Caiming Xiong, Silvio Savarese, and Doyen Sahoo · 2024
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UP2ME: Univariate pre-training to multivariate fine-tuning as a general-purpose framework for multivariate time series analysis
Yunhao Zhang, Minghao Liu, Shengyang Zhou, and Junchi Yan · 2024
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