Kolmogorov-Arnold 网络（KANs）用于时间序列分析
Kolmogorov-Arnold Networks (KANs) for Time Series Analysis
AI总结：
本文提出将Kolmogorov-Arnold网络应用于时间序列预测，利用自适应激活函数和样条参数化权重，在卫星流量预测中以更少参数超越MLP，并进行了消融研究。
摘要：
本文介绍了将Kolmogorov-Arnold网络（KANs）新颖地应用于时间序列预测，利用其自适应激活函数来增强预测建模。受Kolmogorov-Arnold表示定理的启发，KANs用样条参数化的单变量函数替代传统的线性权重，使其能够动态学习激活模式。我们证明，在真实的卫星流量预测任务中，KANs优于传统的多层感知器（MLPs），以显著更少的可学习参数提供了更准确的结果。我们还对KAN特定参数对性能的影响进行了消融研究。所提出的方法为自适应预测模型开辟了新途径，强调了KANs作为预测分析中强大工具的潜力。
英文摘要：
This paper introduces a novel application of Kolmogorov-Arnold Networks (KANs) to time series forecasting, leveraging their adaptive activation functions for enhanced predictive modeling. Inspired by the Kolmogorov-Arnold representation theorem, KANs replace traditional linear weights with spline-parametrized univariate functions, allowing them to learn activation patterns dynamically. We demonstrate that KANs outperforms conventional Multi-Layer Perceptrons (MLPs) in a real-world satellite traffic forecasting task, providing more accurate results with considerably fewer number of learnable parameters. We also provide an ablation study of KAN-specific parameters impact on performance. The proposed approach opens new avenues for adaptive forecasting models, emphasizing the potential of KANs as a powerful tool in predictive analytics.
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。
