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This paper introduces a novel application of Kolmogorov-Arnold Networks (KANs) to time series forecasting, leveraging their adaptive activation functions for enhanced predictive modeling.
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O. B. Sezer, M. U. Gudelek, and A. M. Ozbayoglu, “Financial time series forecasting with deep learning: A systematic literature review: 2005–2019,” Applied soft computing
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I. E. Livieris, E. Pintelas, and P. Pintelas, “A cnn–lstm model for gold price time-series forecasting,” Neural computing and applications
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J. Schmidt-Hieber, “The kolmogorov–arnold representation theorem revisited,” Neural networks
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K. R. Prakarsha and G. Sharma, “Time series signal forecasting using artificial neural networks: An application on ecg signal,” Biomedical Signal Processing and Control
2022
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S. Mehtab and J. Sen, “Analysis and forecasting of financial time series using cnn and lstm-based deep learning models,” in Advances in Distributed Computing and Machine Learning: Proceedings of ICADCML 2021
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Z. Chen, M. Ma, T. Li, H. Wang, and C. Li, “Long sequence time-series forecasting with deep learning: A survey,” Information Fusion
2023
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B. Lim and S. Zohren, “Time-series forecasting with deep learning: a survey,” Philosophical Transactions of the Royal Society A
2021
Cited alongside, same era.
J. F. Torres, D. Hadjout, A. Sebaa, F. Martínez-Álvarez, and A. Troncoso, “Deep learning for time series forecasting: a survey,” Big Data
2021
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2023
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G. Bachmann, S. Anagnostidis, and T. Hofmann, “Scaling mlps: A tale of inductive bias,” Advances in Neural Information Processing Systems
2024
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