Kolmogorov-Arnold Networks (KANs) for Time Series Analysis

The authors in [1] detailed this approach, providing a their adaptive activation functions for enhanced predictive comprehensive methodology foundational for subsequent statistical modeling. Inspired by the Kolmogorov-Arnold representation forecasting methods. Extensions of ARIMA, like Seasonal theorem, KANs replace traditional linear weights with ARIMA (SARIMA), adapt the model to handle seasonality spline-parametrized univariate functions, allowing them to in data series, particularly useful in fields like retail and learn activation patterns dynamically. We demonstrate that climatology [2]. Exponential Smoothing techniques constitute KANs outperforms conventional Multi-Layer Perceptrons another popular set of traditional (non-ML-based) forecasting (MLPs) in a real-world satellite traffic forecasting task, providing methods. They are characterized by their simplicity more accurate results with considerably fewer number and effectiveness in handling data with trends and seasonality. of learnable parameters. We also provide an ablation study of An exponent of this family of techniques is the so-called Holt-KAN-specific parameters impact on performance. The proposed Winters seasonal technique, which adjusts the model parameters approach opens new avenues for adaptive forecasting in response to changes in trend and seasonality within the models, emphasizing the potential of KANs as a powerful time series data [3, 4]. These models have been widely used tool in predictive analytics.