TKAN：时间柯尔莫哥洛夫-阿诺德网络
TKAN: Temporal Kolmogorov-Arnold Networks
AI总结：
针对传统序列模型处理复杂模式的局限，受KAN与LSTM启发提出融合二者优势、含嵌入内存管理RKAN层的TKAN架构，可提升多步时间序列预测的精度与效率，为相关多步预测领域提供新方案。
摘要：
循环神经网络（RNN）已彻底改变机器学习的诸多领域，尤其是在自然语言和数据序列处理方面。长短期记忆网络（LSTM）已展现出捕捉序列数据中长期依赖关系的能力。受柯尔莫哥洛夫-阿诺德网络（KANs，一种颇具前景的多层感知机（MLPs）替代方案）的启发，我们结合KAN与LSTM提出了一种新的神经网络架构——时间柯尔莫哥洛夫-阿诺德网络（TKANs）。TKAN融合了两种网络的优势，由嵌入内存管理的循环柯尔莫哥洛夫-阿诺德网络（RKANs）层构成。这一创新使我们能够以更高的精度和效率执行多步时间序列预测。通过解决传统模型在处理复杂序列模式方面的局限性，TKAN架构在需要多步超前预测的领域具备重大发展潜力。
英文摘要：
Recurrent Neural Networks (RNNs) have revolutionized many areas of machine learning, particularly in natural language and data sequence processing. Long Short-Term Memory (LSTM) has demonstrated its ability to capture long-term dependencies in sequential data. Inspired by the Kolmogorov-Arnold Networks (KANs) a promising alternatives to Multi-Layer Perceptrons (MLPs), we proposed a new neural networks architecture inspired by KAN and the LSTM, the Temporal Kolomogorov-Arnold Networks (TKANs). TKANs combined the strenght of both networks, it is composed of Recurring Kolmogorov-Arnold Networks (RKANs) Layers embedding memory management. This innovation enables us to perform multi-step time series forecasting with enhanced accuracy and efficiency. By addressing the limitations of traditional models in handling complex sequential patterns, the TKAN architecture offers significant potential for advancements in fields requiring more than one step ahead forecasting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
