TSKANMixer：基于Kolmogorov-Arnold网络与MLP-Mixer模型的时间序列预测
TSKANMixer: Kolmogorov-Arnold Networks with MLP-Mixer Model for Time Series Forecasting
AI总结：
本文提出TSKANMixer，将KAN层融入TSMixer，在多个数据集上提升时间序列预测精度，表明KANs可替代或扩展传统MLPs。
摘要：
时间序列预测长期以来一直是经济学、能源、医疗保健和交通管理等多个领域的研究焦点。近期的工作引入了时间序列模型的创新架构，例如时间序列混合器（TSMixer），它利用多层感知机（MLPs）通过有效捕获数据中的空间和时间依赖性来提高预测准确性。在本文中，我们通过将KAN层（TSKANMixer）修改到TSMixer中，研究了Kolmogorov-Arnold网络（KANs）在时间序列预测中的能力。实验结果表明，TSKANMixer在多个数据集上倾向于比原始TSMixer提高预测准确性，与其他时间序列方法相比，排名位居前列。我们的结果表明，通过替换或扩展传统MLPs，KANs是提高时间序列预测性能的有前景的替代方案。
英文摘要：
Time series forecasting has long been a focus of research across diverse fields, including economics, energy, healthcare, and traffic management. Recent works have introduced innovative architectures for time series models, such as the Time-Series Mixer (TSMixer), which leverages multi-layer perceptrons (MLPs) to enhance prediction accuracy by effectively capturing both spatial and temporal dependencies within the data. In this paper, we investigate the capabilities of the Kolmogorov-Arnold Networks (KANs) for time-series forecasting by modifying TSMixer with a KAN layer (TSKANMixer). Experimental results demonstrate that TSKANMixer tends to improve prediction accuracy over the original TSMixer across multiple datasets, ranking among the top-performing models compared to other time series approaches. Our results show that the KANs are promising alternatives to improve the performance of time series forecasting by replacing or extending traditional MLPs.
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。
