# Advancing Real-Estate Forecasting: A Novel Approach Using Kolmogorov-Arnold Networks
## 2 Literature Review and State of the Art  
### KAN-Based Approaches  
[PERSON] et al. introduce KANs, a novel neural network architecture inspired by the Kolmogorov-Arnold representation theorem, and show that it has significant advantages  
Figure 1: KAN architecture.  
over traditional MLPs [9]. KANs are designed to effectively capture compositional structures in data, enabling them to approximate complex functions, such as exponential and sine functions, with greater efficiency and accuracy. The authors demonstrate that KANs not only outperform MLPs in terms of accuracy, particularly in small-scale AI and science tasks, but also enhance interpretability by revealing the underlying compositional structures in symbolic formulas. Furthermore, KANs can utilize grid extension techniques to further improve their performance, positioning them as a promising foundation for future AI applications in scientific discovery and mathematical exploration.  
[PERSON] et al. investigate the potential of KANs as a transformative approach to deep learning, particularly in the context of autoencoders for image representation tasks. The study compares KAN-based autoencoders against traditional convolutional neural networks (CNNs) across several datasets, revealing competitive reconstruction accuracy and suggesting KANs' viability in advanced data analysis applications [10].  
[PERSON] et al. present a novel hybrid model combining KANs and artificial NNs for short-term load forecasting, addressing the limitations of traditional NN approaches. By leveraging KANs to identify essential periodic and nonlinear patterns in load data, while utilizing NNs to fit the residual components, the proposed method enhances both interpretability and predictive accuracy. The integration of regularization techniques ensures that KAN-derived predictions maintain dominance, leading to clearer analytical expressions for forecasting. Experimental validation against various models demonstrates superior performance of the hybrid approach, marking a significant advancement in short-term load forecasting methodologies [11].  
[PERSON] et al. propose the Kolmogorov-Arnold transformer (KAT), an innovative architecture that enhances traditional transformer models by substituting MLP layers with KAN layers [12]. This approach significantly improves expressiveness and performance, addressing key challenges in integrating KANs into transformers and paving the way for more efficient deep learning models.  
[PERSON] et al. present a study focused on enhancing road safety by detecting mobile phone usage among bus drivers [13]. The research utilizes KANs to improve the accuracy of driver monitoring systems in compliance with regulations against phone use while driving.  
[PERSON] et al. introduce a Kolmogorov-Arnold recurrent network (KARN), a novel approach combining KAN's flexibility with RNN's temporal modeling capabilities for improved load forecasting in energy management. Through the integration of learnable temporal spline functions and edge-based activations, KARN addresses the limitations of traditional RNNs and LSTMs in capturing complex load variations. Extensive evaluation across diverse consumer types, including residential and industrial buildings, demonstrates KARN's superior performance and versatility compared to conventional approaches, establishing it as a robust solution for energy-load forecasting [14].  
[PERSON] et al. explore the application of KANs for predicting renewable energy variables, addressing two key challenges in deep learning: interpretability and architectural sensitivity. Using data from the Tokyo Meteorological Observatory, the research demonstrates KAN's effectiveness in predicting solar radiation and outdoor temperature, achieving superior performance even with minimal architecture [15].