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In this paper, we introduce TimeGPT, the first foundation model for time series, capable of generating accurate predictions for diverse datasets not seen during training.
25 years of time series forecasting
Jan G De Gooijer and Rob J Hyndman · 2006
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A tutorial on conformal prediction
Glenn Shafer and Vladimir Vovk · 2008
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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An empirical evaluation of generic convolutional and recurrent networks for sequence modeling
Shaojie Bai, J Zico Kolter, and Vladlen Koltun · 2018
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Forecasting at scale
Sean J Taylor and Benjamin Letham · 2018
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N-beats: Neural basis expansion analysis for interpretable time series forecasting
Boris N Oreshkin, Dmitri Carpov, Nicolas Chapados, and Yoshua Bengio · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Mqtransformer: Multi-horizon forecasts with context dependent and feedback-aware attention
Carson Eisenach, Yagna Patel, and Dhruv Madeka · 2020
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Criteria for classifying forecasting methods
Tim Januschowski, Jan Gasthaus, Yuyang Wang, David Salinas, Valentin Flunkert, Michael Bohlke-Schneider, and Laurent Callot · 2020
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The m4 competition: 100,000 time series and 61 forecasting methods
Spyros Makridakis, Evangelos Spiliotis, and Vassilios Assimakopoulos · 2020
Cited alongside, same era.
Deepar: Probabilistic forecasting with autoregressive recurrent networks
David Salinas, Valentin Flunkert, Jan Gasthaus, and Tim Januschowski · 2020
Cited alongside, same era.
A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting
Slawek Smyl · 2020
Cited alongside, same era.
Temporal fusion transformers for interpretable multi-horizon time series forecasting
Bryan Lim, Sercan Ö Arık, Nicolas Loeff, and Tomas Pfister · 2021
Cited alongside, same era.
Meta-learning framework with applications to zero-shot time-series forecasting
Boris N Oreshkin, Dmitri Carpov, Nicolas Chapados, and Yoshua Bengio · 2021
Cited alongside, same era.
Conformal time-series forecasting
M5 accuracy competition: Results, findings, and conclusions
Spyros Makridakis, Evangelos Spiliotis, and Vassilios Assimakopoulos · 2022
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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 · 2022
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Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with nbeatsx
Kin G Olivares, Cristian Challu, Grzegorz Marcjasz, Rafał Weron, and Artur Dubrawski · 2022
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Timesnet: Temporal 2d-variation modeling for general time series analysis
Haixu Wu, Tengge Hu, Yong Liu, Hang Zhou, Jianmin Wang, and Mingsheng Long · 2022
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Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting
Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin · 2022
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Kamile Stankeviciute, Ahmed M Alaa, and Mihaela van der Schaar · 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.
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.
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, François-Xavier Aubet, Laurent Callot, and Tim Januschowski · 2022
Cited alongside, same era.
Transferability of neural forecast models
Kin G Olivares, Cristian Challu, Federico Garza Ramirez, Max Mergenthaler Canseco, and Artur Dubrawski
Cited in the paper.
Probabilistic hierarchical forecasting with deep poisson mixtures
Kin G Olivares, O Nganba Meetei, Ruijun Ma, Rohan Reddy, Mengfei Cao, and Lee Dicker
Cited in the paper.
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Nhits: Neural hierarchical interpolation for time series forecasting
Cristian Challu, Kin G Olivares, Boris N Oreshkin, Federico Garza Ramirez, Max Mergenthaler Canseco, and Artur Dubrawski · 2023
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Deep learning based forecasting: a case study from the online fashion industry
Manuel Kunz, Stefan Birr, Mones Raslan, Lei Ma, Zhen Li, Adele Gouttes, Mateusz Koren, Tofigh Naghibi, Johannes Stephan, Mariia Bulycheva, et al · 2023
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Are transformers effective for time series forecasting?
Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu · 2023
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