C-KAN: A New Approach for Integrating Convolutional Layers with Kolmogorov–Arnold Networks for Time-Series Forecasting

Time-series forecasting represents of one of the most challenging and widely studied research areas in both academic and industrial communities. Despite the recent advancements in deep learning, the prediction of future time-series values remains a considerable endeavor due to the complexity and dynamic nature of time-series data. In this work, a new prediction model is proposed, named C-KAN, for multi-step forecasting, which is based on integrating convolutional layers with Kolmogorov–Arnold network architecture. The proposed model’s advantages are (i) the utilization of convolutional layers for learning the behavior and internal representation of time-series input data; (ii) activation at the edges of the Kolmogorov–Arnold network for potentially altering training dynamics; and (iii) modular non-linearity for allowing the differentiated treatment of features and potentially more precise control over inputs’ influence on outputs. Furthermore, the proposed model is trained using the DILATE loss function, which ensures that it is able to effectively deal with the dynamics and high volatility of non-stationary time-series data. The numerical experiments and statistical analysis were conducted on five challenging non-stationary time-series datasets, and provide strong evidence that C-KAN constitutes an efficient and accurate model, well suited for time-series forecasting tasks.