Fetching the paper…
Reading the bibliography…
Motivated by recent advances in large language models for Natural Language Processing (NLP), we design a time-series foundation model for forecasting whose out-of-the-box zero-shot performance on a variety of public datasets comes close to the accuracy of state-of-the-art supervised forecasting models for each individual dataset.
Some recent advances in forecasting and control
George EP Box and Gwilym M Jenkins · 1968
Earlier work this paper cites.
General exponential smoothing and the equivalent arma process
ED McKenzie · 1984
Earlier work this paper cites.
How not to lie with statistics: the correct way to summarize benchmark results
Philip J Fleming and John J Wallace · 1986
Earlier work this paper cites.
Vector autoregressive models for multivariate time series
Eric Zivot and Jiahui Wang · 2006
Earlier work this paper cites.
Gaussian process kernels for pattern discovery and extrapolation
Andrew Wilson and Ryan Adams · 2013
Earlier work this paper cites.
Temporal convolutional networks: A unified approach to action segmentation
Colin Lea, Rene Vidal, Austin Reiter, and Gregory D Hager · 2016
Earlier work this paper cites.
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
Earlier work this paper cites.
Conditional time series forecasting with convolutional neural networks
Anastasia Borovykh, Sander Bohte, and Cornelis W Oosterlee · 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
Earlier work this paper cites.
A multi-horizon quantile recurrent forecaster
Ruofeng Wen, Kari Torkkola, Balakrishnan Narayanaswamy, and Dhruv Madeka · 2017
Earlier work this paper cites.
Generating wikipedia by summarizing long sequences
Peter J Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, and Noam Shazeer · 2018
Earlier work this paper cites.
Catboost: unbiased boosting with categorical features
Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, and Andrey Gulin · 2018
Earlier work this paper cites.
Forecasting at scale
Sean J Taylor and Benjamin Letham · 2018
Earlier work this paper cites.
Model cards for model reporting
Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru · 2019
Earlier work this paper cites.
N-beats: Neural basis expansion analysis for interpretable time series forecasting
Boris N Oreshkin, Dmitri Carpov, Nicolas Chapados, and Yoshua Bengio · 2019
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
Cited alongside, same era.
Think globally, act locally: A deep neural network approach to high-dimensional time series forecasting
Rajat Sen, Hsiang-Fu Yu, and Inderjit S Dhillon · 2019
Cited alongside, same era.
Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 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.
On the benefits of maximum likelihood estimation for regression and forecasting
Pranjal Awasthi, Abhimanyu Das, Rajat Sen, and Ananda Theertha Suresh · 2021
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
Later among the works it cites.
Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting
Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin · 2022
Later among the works it cites.
Tsmixer: An all-mlp architecture for time series forecasting
Si-An Chen, Chun-Liang Li, Nate Yoder, Sercan O Arik, and Tomas Pfister · 2023
Closest in time.
NHITS: Neural Hierarchical Interpolation for Time Series forecasting
Cristian Challu, Kin G. Olivares, Boris N. Oreshkin, Federico Garza, Max Mergenthaler, and Artur Dubrawski · 2023
Closest in time.
Llm4ts: Two-stage fine-tuning for time-series forecasting with pre-trained llms
Ching Chang, Wen-Chih Peng, and Tien-Fu Chen · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Monash time series forecasting archive
Rakshitha Godahewa, Christoph Bergmeir, Geoffrey I Webb, Rob J Hyndman, and Pablo Montero-Manso · 2021
Cited alongside, same era.
Traffic4cast at neurips 2020 - yet more on the unreasonable effectiveness of gridded geo-spatial processes
Michael Kopp, David Kreil, Moritz Neun, David Jonietz, Henry Martin, Pedro Herruzo, Aleksandra Gruca, Ali Soleymani, Fanyou Wu, Yang Liu, Jingwei Xu, Jianjin Zhang, Jay Santokhi, Alabi Bojesomo, Hasan Al Marzouqi, Panos Liatsis, Pak Hay Kwok, Qi Qi, and Sepp Hochreiter · 2021
Cited alongside, same era.
Reversible instance normalization for accurate time-series forecasting against distribution shift
Taesung Kim, Jinhee Kim, Yunwon Tae, Cheonbok Park, Jang-Ho Choi, and Jaegul Choo · 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.
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.
Training compute-optimal large language models
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, et al · 2022
Cited alongside, same era.
Closest in time.
Long-term forecasting with TiDE: Time-series dense encoder
Abhimanyu Das, Weihao Kong, Andrew Leach, Shaan K Mathur, Rajat Sen, and Rose Yu · 2023
Closest in time.
Mamba: Linear-time sequence modeling with selective state spaces
Albert Gu and Tri Dao · 2023
Closest in time.
Large language models are zero-shot time series forecasters
Nate Gruver, Marc Finzi, Shikai Qiu, and Andrew Gordon Wilson · 2023
Closest in time.
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
Closest in time.
Feature importance: A closer look at shapley values and loco
Isabella Verdinelli and Larry Wasserman · 2023
Closest in time.
Jingyuan Wang, Jiawei Jiang, Wenjun Jiang, Chengkai Han, and Wayne Xin Zhao · 2023
Closest in time.
Are transformers effective for time series forecasting?
Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu · 2023
Closest in time.
One fits all: Power general time series analysis by pretrained lm
Tian Zhou, Peisong Niu, Xue Wang, Liang Sun, and Rong Jin · 2023
Closest in time.