This paper introduces Kolmogorov-Arnold Networks (KANs) for time series forecasting, leveraging their adaptive activation functions to enhance predictive modeling. Inspired by the Kolmogorov-Arnold representation theorem, KANs replace traditional linear weights with spline-parametrized univariate functions, allowing dynamic learning of activation patterns. The authors demonstrate that KANs outperform conventional Multi-Layer Perceptrons (MLPs) in a real-world satellite traffic forecasting task, providing more accurate results with fewer learnable parameters. An ablation study evaluates the impact of KAN-specific parameters on performance. The proposed approach opens new avenues for adaptive forecasting models, emphasizing the potential of KANs as a powerful tool in predictive analytics. The study highlights the benefits of KANs, including superior forecasting performance and greater parameter efficiency, making them a reasonable alternative to traditional MLPs in traffic management.This paper introduces Kolmogorov-Arnold Networks (KANs) for time series forecasting, leveraging their adaptive activation functions to enhance predictive modeling. Inspired by the Kolmogorov-Arnold representation theorem, KANs replace traditional linear weights with spline-parametrized univariate functions, allowing dynamic learning of activation patterns. The authors demonstrate that KANs outperform conventional Multi-Layer Perceptrons (MLPs) in a real-world satellite traffic forecasting task, providing more accurate results with fewer learnable parameters. An ablation study evaluates the impact of KAN-specific parameters on performance. The proposed approach opens new avenues for adaptive forecasting models, emphasizing the potential of KANs as a powerful tool in predictive analytics. The study highlights the benefits of KANs, including superior forecasting performance and greater parameter efficiency, making them a reasonable alternative to traditional MLPs in traffic management.