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The vast majority of time-series forecasting approaches require a substantial training dataset.
Distribution of residual autocorrelations in autoregressive-integrated moving average time series models
George EP Box and David A Pierce · 1970
Earlier work this paper cites.
Time-series forecasting
Chris Chatfield · 2000
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Arima models to predict next-day electricity prices
Javier Contreras, Rosario Espinola, Francisco J Nogales, and Antonio J Conejo · 2003
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25 years of time series forecasting
Jan G De Gooijer and Rob J Hyndman · 2006
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Application of machine learning techniques for supply chain demand forecasting
Real Carbonneau, Kevin Laframboise, and Rustam Vahidov · 2008
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Financial time series forecasting with machine learning techniques: a survey
Bjoern Krollner, Bruce J Vanstone, Gavin R Finnie, et al · 2010
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Demand forecasting in the fashion industry: a review
Maria Elena Nenni, Luca Giustiniano, and Luca Pirolo · 2013
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Empirical evaluation of gated recurrent neural networks on sequence modeling
Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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ElectricityLoadDiagrams20112014
Artur Trindade · 2015
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pmdarima: Arima estimators for Python, 2017–
Taylor G. Smith et al · 2017
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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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A multi-horizon quantile recurrent forecaster
Ruofeng Wen, Kari Torkkola, Balakrishnan Narayanaswamy, and Dhruv Madeka · 2017
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Transfer learning for time series classification
Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber, Lhassane Idoumghar, and Pierre-Alain Muller · 2018
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Forecasting: principles and practice
Rob J Hyndman and George Athanasopoulos · 2018
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Modeling long-and short-term temporal patterns with deep neural networks
Guokun Lai, Wei-Cheng Chang, Yiming Yang, and Hanxiao Liu · 2018
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Deep state space models for time series forecasting
Syama Sundar Rangapuram, Matthias W Seeger, Jan Gasthaus, Lorenzo Stella, Yuyang Wang, and Tim Januschowski · 2018
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Forecasting at scale
Sean J Taylor and Benjamin Letham · 2018
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Sales prediction through neural networks for a small dataset
Rosa María Cantón Croda, Damián Emilio Gibaja Romero, and Santiago Omar Caballero Morales · 2019
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Machine learning vs statistical methods for time series forecasting: Size matters
Vitor Cerqueira, Luis Torgo, and Carlos Soares · 2019
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Multitask learning and benchmarking with clinical time series data
Hrayr Harutyunyan, Hrant Khachatrian, David C Kale, Greg Ver Steeg, and Aram Galstyan · 2019
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Deep learning for time series classification: a review
Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber, Lhassane Idoumghar, and Pierre-Alain Muller · 2019
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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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Longformer: The long-document transformer
Iz Beltagy, Matthew E. Peters, and Arman Cohan · 2020
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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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Time series forecasting of covid-19 transmission in canada using lstm networks
Vinay Kumar Reddy Chimmula and Lei Zhang · 2020
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Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting
Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long · 2021
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Implementing transfer learning across different datasets for time series forecasting
Rui Ye and Qun Dai · 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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Flashattention: Fast and memory-efficient exact attention with io-awareness
Tri Dao, Dan Fu, Stefano Ermon, Atri Rudra, and Christopher Ré · 2022
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Global models for time series forecasting: A simulation study
Hansika Hewamalage, Christoph Bergmeir, and Kasun Bandara · 2022
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Transformers can do bayesian inference
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Autogluon-tabular: Robust and accurate automl for structured data
Nick Erickson, Jonas Mueller, Alexander Shirkov, Hang Zhang, Pedro Larroy, Mu Li, and Alexander Smola · 2020
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Simon James Fong, Gloria Li, Nilanjan Dey, Rubén González Crespo, and Enrique Herrera-Viedma · 2020
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National, regional, and state level outpatient illness and viral surveillance
Centers for Disease Control and Prevention · 2020
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A critical review of wind power forecasting methods—past, present and future
Shahram Hanifi, Xiaolei Liu, Zi Lin, and Saeid Lotfian · 2020
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Reformer: The efficient transformer
Nikita Kitaev, Lukasz Kaiser, and Anselm Levskaya · 2020
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The m4 competition: 100,000 time series and 61 forecasting methods
Spyros Makridakis, Evangelos Spiliotis, and Vassilios Assimakopoulos · 2020
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Zero-shot and few-shot time series forecasting with ordinal regression recurrent neural networks
Bernardo Pérez Orozco and Stephen J Roberts · 2020
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Samuel Müller, Noah Hollmann, Sebastian Pineda Arango, Josif Grabocka, and Frank Hutter · 2022
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Jaideep Pathak, Shashank Subramanian, Peter Harrington, Sanjeev Raja, Ashesh Chattopadhyay, Morteza Mardani, Thorsten Kurth, David Hall, Zongyi Li, Kamyar Azizzadenesheli, et al · 2022
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Efficient transformers: A survey
Yi Tay, Mostafa Dehghani, Dara Bahri, and Donald Metzler · 2022
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Sdwpf: A dataset for spatial dynamic wind power forecasting challenge at kdd cup 2022
Jingbo Zhou, Xinjiang Lu, Yixiong Xiao, Jiantao Su, Junfu Lyu, Yanjun Ma, and Dejing Dou · 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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Efficient bayesian learning curve extrapolation using prior-data fitted networks
Steven Adriaensen, Herilalaina Rakotoarison, Samuel Müller, and Frank Hutter · 2023
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Large language models are zero-shot time series forecasters
Nate Gruver, Marc Finzi, Shikai Qiu, and Andrew Gordon Wilson · 2023
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Tabpfn: A transformer that solves small tabular classification problems in a second
Noah Hollmann, Samuel Müller, Katharina Eggensperger, and Frank Hutter · 2023
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Gpt for semi-automated data science: Introducing caafe for context-aware automated feature engineering
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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
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When do neural nets outperform boosted trees on tabular data?
Duncan McElfresh, Sujay Khandagale, Jonathan Valverde, Ganesh Ramakrishnan, Vishak Prasad, Micah Goldblum, and Colin White · 2023
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Pfns4bo: In-context learning for bayesian optimization
Samuel Müller, Matthias Feurer, Noah Hollmann, and Frank Hutter · 2023
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Statistical foundations of prior-data fitted networks
Thomas Nagler · 2023
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Lag-llama: Towards foundation models for 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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Autogluon-timeseries: Automl for probabilistic time series forecasting
Oleksandr Shchur, Caner Turkmen, Nick Erickson, Huibin Shen, Alexander Shirkov, Tony Hu, and Yuyang Wang · 2023
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