Advancing Real-Estate Forecasting: A Novel Approach Using Kolmogorov–Arnold Networks DOI Creative Commons

Iosif Viktoratos,

Athanasios Tsadiras

Algorithms, Год журнала: 2025, Номер 18(2), С. 93 - 93

Опубликована: Фев. 7, 2025

Accurately estimating house values is a critical challenge for real-estate stakeholders, including homeowners, buyers, sellers, agents, and policymakers. This study introduces novel approach to this problem using Kolmogorov–Arnold networks (KANs), type of neural network based on the theorem. The proposed KAN model was tested two datasets demonstrated superior performance compared existing state-of-the-art methods predicting prices. By delivering more precise price forecasts, supports improved decision-making stakeholders. Additionally, results highlight broader potential KANs addressing complex prediction tasks in data science. aims provide an innovative effective solution accurate estimation, offering significant benefits industry beyond.

Язык: Английский

KAN–CNN: A Novel Framework for Electric Vehicle Load Forecasting with Enhanced Engineering Applicability and Simplified Neural Network Tuning DOI Open Access

Zhigang Pei,

Zhiyuan Zhang, Jiaming Chen

и другие.

Electronics, Год журнала: 2025, Номер 14(3), С. 414 - 414

Опубликована: Янв. 21, 2025

Electric Vehicle (EV) load forecasting is critical for optimizing resource allocation and ensuring the stability of modern energy systems. However, traditional machine learning models, predominantly based on Multi-Layer Perceptrons (MLPs), encounter substantial challenges in modeling complex, nonlinear, dynamic patterns inherent EV charging data, often leading to overfitting high computational costs. To overcome these limitations, this study introduces KAN–CNN, a novel hybrid architecture that integrates Kolmogorov–Arnold Networks (KANs) into frameworks, specifically Convolutional Neural (CNNs). By combining spatial feature extraction strength CNNs with adaptive nonlinearity KAN, KAN–CNN achieves superior representation flexibility. The key innovations include bottleneck KAN convolutional layers reducing parameter complexity, Self-Attention Network Global Nonlinearity (Self-KAGN) Attention enhance global dependency modeling, Focal KAGN Modulation refinement. Furthermore, regularization techniques such as L1/L2 penalties, dropout, Gaussian noise injection are utilized model’s robustness generalization capability. When applied forecasting, demonstrates prediction accuracy comparable state-of-the-art methods while significantly overhead simplifying tuning. This work bridges gap between theoretical practical applications, offering robust efficient solution system challenges.

Язык: Английский

Процитировано

2
SOH-KLSTM: A hybrid Kolmogorov-Arnold Network and LSTM model for enhanced Lithium-ion battery Health Monitoring DOI
Imen Jarraya, Safa Ben Atitallah, Fatimah Alahmed

и другие.

Journal of Energy Storage, Год журнала: 2025, Номер 122, С. 116541 - 116541

Опубликована: Апрель 15, 2025

Язык: Английский

Процитировано

0
Advancing Real-Estate Forecasting: A Novel Approach Using Kolmogorov–Arnold Networks DOI Creative Commons

Iosif Viktoratos,

Athanasios Tsadiras

Algorithms, Год журнала: 2025, Номер 18(2), С. 93 - 93

Опубликована: Фев. 7, 2025

Accurately estimating house values is a critical challenge for real-estate stakeholders, including homeowners, buyers, sellers, agents, and policymakers. This study introduces novel approach to this problem using Kolmogorov–Arnold networks (KANs), type of neural network based on the theorem. The proposed KAN model was tested two datasets demonstrated superior performance compared existing state-of-the-art methods predicting prices. By delivering more precise price forecasts, supports improved decision-making stakeholders. Additionally, results highlight broader potential KANs addressing complex prediction tasks in data science. aims provide an innovative effective solution accurate estimation, offering significant benefits industry beyond.

Язык: Английский

Процитировано

0