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By encoding time series as a string of numerical digits, we can frame time series forecasting as next-token prediction in text.
Some recent advances in forecasting and control
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Forecasting with exponential smoothing: the state space approach
Rob J Hyndman, Anne B Koehler, J Keith Ord, and Ralph D Snyder · 2008
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A scalable hierarchical distributed language model
Andriy Mnih and Geoffrey E Hinton · 2008
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Gaussian process kernels for pattern discovery and extrapolation
Andrew Wilson and Ryan Adams · 2013
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On the identification of sales forecasting models in the presence of promotions
Juan R Trapero, Nikolaos Kourentzes, and Robert Fildes · 2015
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Deep learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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Temporal convolutional networks: A unified approach to action segmentation
Colin Lea, Rene Vidal, Austin Reiter, and Gregory D Hager · 2016
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Wavenet: A generative model for raw audio
Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu · 2016
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Deep reinforcement learning from human preferences
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei · 2017
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imputets: time series missing value imputation in r
Steffen Moritz and Thomas Bartz-Beielstein · 2017
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Gpytorch: Blackbox matrix-matrix gaussian process inference with gpu acceleration
Jacob R Gardner, Geoff Pleiss, David Bindel, Kilian Q Weinberger, and Andrew Gordon Wilson · 2018
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Catboost: unbiased boosting with categorical features
Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, and Andrey Gulin · 2018
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The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi · 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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Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt · 2020
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Ai in healthcare: time-series forecasting using statistical, neural, and ensemble architectures
Shruti Kaushik, Abhinav Choudhury, Pankaj Kumar Sheron, Nataraj Dasgupta, Sayee Natarajan, Larry A Pickett, and Varun Dutt · 2020
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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 · 2020
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Deepar: Probabilistic forecasting with autoregressive recurrent networks
David Salinas, Valentin Flunkert, Jan Gasthaus, and Tim Januschowski · 2020
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Monash time series forecasting archive
Rakshitha Godahewa, Christoph Bergmeir, Geoffrey I Webb, Rob J Hyndman, and Pablo Montero-Manso · 2021
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Deduplicating training data makes language models better
Katherine Lee, Daphne Ippolito, Andrew Nystrom, Chiyuan Zhang, Douglas Eck, Chris Callison-Burch, and Nicholas Carlini · 2021
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Show your work: Scratchpads for intermediate computation with language models
Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari, Henryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, et al · 2021
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Can you learn an algorithm? generalizing from easy to hard problems with recurrent networks
Avi Schwarzschild, Eitan Borgnia, Arjun Gupta, Furong Huang, Uzi Vishkin, Micah Goldblum, and Tom Goldstein · 2021
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Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Y Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le · 2021
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Colt5: Faster long-range transformers with conditional computation
Joshua Ainslie, Tao Lei, Michiel de Jong, Santiago Ontañón, Siddhartha Brahma, Yury Zemlyanskiy, David Uthus, Mandy Guo, James Lee-Thorp, Yi Tay, et al · 2023
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Introducing 100k context windows
Anthropic · 2023
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Emergent and predictable memorization in large language models
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Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long · 2021
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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
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Path independent equilibrium models can better exploit test-time computation
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Deep probabilistic time series forecasting over long horizons
Gregory Benton, Nate Gruver, Wesley Maddox, and Andrew Gordon Wilson · 2022
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N-hits: Neural hierarchical interpolation for time series forecasting
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Dazhao Du, Bing Su, and Zhewei Wei · 2022
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The lie derivative for measuring learned equivariance
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Sparks of artificial general intelligence: Early experiments with gpt-4
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