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Kolmogorov-Arnold Networks (KAN) is a groundbreaking model recently proposed by the MIT team, representing a revolutionary approach with the potential to be a game-changer in the field.
On the representation of continuous functions of many variables by superposition of continuous functions of one variable and addition
Andrei Nikolaevich Kolmogorov · 1957
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On the representation of continuous functions of several variables by superpositions of continuous functions of a smaller number of variables
Andrei Nikolaevich Kolmogorov · 1961
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On a constructive proof of kolmogorov’s superposition theorem
Jürgen Braun and Michael Griebel · 2009
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Long short-term memory
Alex Graves and Alex Graves · 2012
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Recent advances in recurrent neural networks
Hojjat Salehinejad, Sharan Sankar, Joseph Barfett, Errol Colak, and Shahrokh Valaee · 2017
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Extract interpretability-accuracy balanced rules from artificial neural networks: A review
Congjie He, Meng Ma, and Ping Wang · 2020
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Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting
Yunhao Zhang and Junchi Yan · 2022
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Neural basis models for interpretability
Filip Radenovic, Abhimanyu Dubey, and Dhruv Mahajan · 2022
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Learning fast and slow for online time series forecasting
Quang Pham, Chenghao Liu, Doyen Sahoo, and Steven CH Hoi · 2022
Cited alongside, same era.
Multi-layer perceptrons
Rudolf Kruse, Sanaz Mostaghim, Christian Borgelt, Christian Braune, and Matthias Steinbrecher · 2022
Cited alongside, same era.
Card: Channel aligned robust blend transformer for time series forecasting
Xue Wang, Tian Zhou, Qingsong Wen, Jinyang Gao, Bolin Ding, and Rong Jin · 2023
Cited alongside, same era.
Learning deep time-index models for time series forecasting
Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, and Steven Hoi · 2023
Cited alongside, same era.
Time-llm: Time series forecasting by reprogramming large language models
Ming Jin, Shiyu Wang, Lintao Ma, Zhixuan Chu, James Y Zhang, Xiaoming Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-Fang Li, Shirui Pan, et al · 2023
Cited alongside, same era.
Large language models are zero-shot time series forecasters
Nate Gruver, Marc Finzi, Shikai Qiu, and Andrew G Wilson · 2024
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Rhine: A regime-switching model with nonlinear representation for discovering and forecasting regimes in financial markets
Kunpeng Xu, Lifei Chen, Jean-Marc Patenaude, and Shengrui Wang · 2024
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Kernel representation learning with dynamic regime discovery for time series forecasting
Kunpeng Xu, Lifei Chen, Jean-Marc Patenaude, and Shengrui Wang · 2024
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Concept drift adaptation by exploiting drift type
Jinpeng Li, Hang Yu, Zhenyu Zhang, Xiangfeng Luo, and Shaorong Xie · 2024
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Onenet: Enhancing time series forecasting models under concept drift by online ensembling
Qingsong Wen, Weiqi Chen, Liang Sun, Zhang Zhang, Liang Wang, Rong Jin, Tieniu Tan, et al · 2024
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Timemixer: Decomposable multiscale mixing for time series forecasting
Shiyu Wang, Haixu Wu, Xiaoming Shi, Tengge Hu, Huakun Luo, Lintao Ma, James Y Zhang, and Jun Zhou · 2024
Cited alongside, same era.
Msgnet: Learning multi-scale inter-series correlations for multivariate time series forecasting
Wanlin Cai, Yuxuan Liang, Xianggen Liu, Jianshuai Feng, and Yuankai Wu · 2024
Cited alongside, same era.
Unitime: A language-empowered unified model for cross-domain time series forecasting
Xu Liu, Junfeng Hu, Yuan Li, Shizhe Diao, Yuxuan Liang, Bryan Hooi, and Roger Zimmermann · 2024
Cited alongside, same era.
Ziming Liu, Yixuan Wang, Sachin Vaidya, Fabian Ruehle, James Halverson, Marin Soljačić, Thomas Y Hou, and Max Tegmark · 2024
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Kolmogorov-arnold networks (kans) for time series analysis
Cristian J Vaca-Rubio, Luis Blanco, Roberto Pereira, and Màrius Caus · 2024
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Tkan: Temporal kolmogorov-arnold networks
Remi Genet and Hugo Inzirillo · 2024
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