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In order to support the advancement of machine learning methods for predicting time-series data, we present a comprehensive dataset designed explicitly for long-term time-series forecasting.
Oscillation and chaos in physiological control systems
Michael C. Mackey and Leon Glass · 1977
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Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012
Ikaro Silva, George B. Moody, Daniel J. Scott, Leo Anthony Celi, and Roger G. Mark · 2012
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Xgboost: A scalable tree boosting system
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The ucr time series archive
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The m4 competition: 100,000 time series and 61 forecasting methods
Spyros Makridakis, Evangelos Spiliotis, and Vassilios Assimakopoulos · 2019
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Latent ordinary differential equations for irregularly-sampled time series
Yulia Rubanova, Ricky T. Q. Chen, and David K Duvenaud · 2019
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Deepar: Probabilistic forecasting with autoregressive recurrent networks
David Salinas, Valentin Flunkert, Jan Gasthaus, and Tim Januschowski · 2019
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D4rl: Datasets for deep data-driven reinforcement learning, 2020
Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine · 2020
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José F. Torres, Dalil Hadjout, Abderrazak Sebaa, Francisco Martínez-Álvarez, and Alicia Troncoso · 2020
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N-hits: Neural hierarchical interpolation for time series forecasting, 2022
Cristian Challu, Kin G. Olivares, Boris N. Oreshkin, Federico Garza, Max Mergenthaler-Canseco, and Artur Dubrawski · 2022
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Data-driven reduced-order modeling of spatiotemporal chaos with neural ordinary differential equations
Alec J. Linot and Michael D. Graham · 2022
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https://drive.google.com/drive/folders/1S16bDiihE6xARB1uMN1AeWq5ByCqkTMI?usp=drive_link
Long-horizon forecasting benchmark code · 2023
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https://drive.google.com/drive/folders/1IfAHpka3hu2kM4j6ebzAPnGpLpSMnTlf?usp=drive_link
Long-horizon forecasting benchmark datasets · 2023
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URL https://archive.ics.uci.edu/ml/datasets/ElectricityLoadDiagrams20112014
Uci machine learning repository: Electricityloaddiagrams20112014 data set · 2023
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Chaos as an interpretable benchmark for forecasting and data-driven modelling
William Gilpin · 2021
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Monash time series forecasting archive
Rakshitha Wathsadini Godahewa, Christoph Bergmeir, Geoffrey I. Webb, Rob Hyndman, and Pablo Montero-Manso · 2021
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An experimental review on deep learning architectures for time series forecasting
Pedro Lara-Benítez, Manuel Carranza-García, and José C. Riquelme · 2021
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Long range arena : A benchmark for efficient transformers
Yi Tay, Mostafa Dehghani, Samira Abnar, Yikang Shen, Dara Bahri, Philip Pham, Jinfeng Rao, Liu Yang, Sebastian Ruder, and Donald Metzler · 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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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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Tinystories: How small can language models be and still speak coherent english?, 2023
Ronen Eldan and Yuanzhi Li · 2023
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Textbooks are all you need, 2023
Suriya Gunasekar, Yi Zhang, Jyoti Aneja, Caio César Teodoro Mendes, Allie Del Giorno, Sivakanth Gopi, Mojan Javaheripi, Piero Kauffmann, Gustavo de Rosa, Olli Saarikivi, Adil Salim, Shital Shah, Harkirat Singh Behl, Xin Wang, Sébastien Bubeck, Ronen Eldan, Adam Tauman Kalai, Yin Tat Lee, and Yuanzhi Li · 2023
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Effectively modeling time series with simple discrete state spaces
Michael Zhang, Khaled Kamal Saab, Michael Poli, Tri Dao, Karan Goel, and Christopher Re · 2023
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One fits all:power general time series analysis by pretrained lm, 2023
Tian Zhou, PeiSong Niu, Xue Wang, Liang Sun, and Rong Jin · 2023
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