用柯尔莫哥洛夫网络替代MLP，提升时间序列预测精度
TSKANMixer: Kolmogorov-Arnold Networks with MLP-Mixer Model for Time Series Forecasting
- 将KAN网络嵌入TSMixer架构，替代原有MLP层
- 在多个数据集上优于原始TSMixer，表现跻身顶尖模型
- 为传统MLP提供有效替代方案，适合时序建模研究者
时间序列预测长期是经济、能源、医疗和交通管理等领域的研究重点。近期工作提出了创新的时序模型架构，如时间序列混合器（TSMixer），通过多层感知机（MLPs）有效捕捉数据中的空间与时间依赖关系，提升预测准确性。本文通过在TSMixer中引入柯尔莫哥洛夫-阿诺德网络（KAN）层，提出TSKANMixer模型。实验结果表明，TSKANMixer在多个数据集上均优于原始TSMixer，性能位列主流时序方法前列。结果证明，KAN网络可作为传统MLP的有效替代或扩展，显著提升时间序列预测表现。
原文摘要 · Abstract (English)
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.
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