Variable Selection in High-Dimensional Solar Radiation Data Using a Hybrid Approach Based on LASSO, Elastic Net, and Fuzzy Logic
DOI:
https://doi.org/10.31272/jae.i153.1610Keywords:
Elastic Net (EN), LASSO, Fuzzy-LASSO, Fuzzy-ENAbstract
Methods for selecting and predicting variables in high-dimensional time series data face numerous challenges due to the sheer number of variables. This makes variable selection difficult and reduces prediction accuracy. Climate variables, for example, are characterised by complexity, nonlinearity, and high correlations among variables, which limit the effectiveness of traditional models in handling high-dimensional and uncertain data. This study aims to build a hybrid predictive framework that combines conventional regularisation methods. LASSO regression and Elastic Net )EN) regression are methods used to reduce the number of explanatory variables for high-dimensional data, which will be used in this study to predict and reduce the number of explanatory climate variables, and between fuzzy logic, by building hybrid LASSO-Fuzzy and EN-Fuzzy models to improve the accuracy of prediction and the efficiency of variable selection by solving the problem of high dimensions, uncertainty and non-linearity, which are among the problems that the data are affected by, High-dimensional solar radiation time series data will be used for the application using the proposed methods. The importance of the hybrid approach lies in reducing the number of variables in high-dimensional solar radiation data, thereby reducing prediction error and improving the model's interpretability. The results showed that hybrid methods outperformed conventional models such as LASSO and EN. Hybrid models have achieved the lowest predictive error rates and more stable performance in selecting and minimising influential variables in high-dimensional data. It can be concluded that the LASSO-Fuzzy and EN-Fuzzy hybrid models improve prediction accuracy for high-dimensional data by effectively reducing dimensionality and selecting the most influential variables.
Downloads
References
[1] R. Tibshirani, "Regression shrinkage and selection via the lasso," Journal of the Royal Statistical Society Series B: Statistical Methodology, vol. 58, pp. 267-288, 1996. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x
[2] H. Zou and T. Hastie, "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B: Statistical Methodology, vol. 67, pp. 301-320, 2005.https://doi.org/10.1111/j.1467-9868.2005.00503.x
[3] V. Plakandaras, R. Gupta, P. Gogas, and T. Papadimitriou, "Forecasting the US real house price index," Economic Modelling, vol. 45, pp. 259-267, 2015.https://doi.org/10.1016/j.econmod.2014.10.050
[4] L. A. Zadeh, "Fuzzy sets," Information and control, vol. 8, pp. 338-353, 1965.https://doi.org/10.1016/S0019-9958(65)90241-X
[5] H. Asai, S. Tanaka, and K. Uegima, "Linear regression analysis with fuzzy model," IEEE Trans. Systems Man Cybern, vol. 12, pp. 903-907, 1982.https://doi.org/10.1109/TSMC.1982.4308925
[6] R. Pereira, A. Fagundes, R. Melicio, V. Mendes, J. Figueiredo, and J. Quadrado, "Fuzzy subtractive clustering technique applied to demand response in a smart grid scope," Procedia Technology, vol. 17, pp. 478-486, 2014.https://doi.org/10.1016/j.protcy.2014.10.256
[7] A. Mustikasari and A. D. Pahrany, "Lasso Regression and Elastic Net in Analysing Factors Affecting the Open Unemployment Rate," Jurnal Varian, vol. 8, pp. 151-164, 2025.https://doi.org/10.30812/xey50x64
[8] A. E. Hoerl and R. W. Kennard, "Ridge regression: applications to nonorthogonal problems," Technometrics, vol. 12, pp. 69-82, 1970.https://doi.org/10.1080/00401706.1970.10488635
[9] A. R. Nur, A. K. Jaya, and S. Siswanto, "Comparative analysis of ridge, lasso, and elastic net regularization approaches in handling multicollinearity for infant mortality data in south sulawesi," Jurnal Matematika, Statistika dan Komputasi, vol. 20, pp. 311-319, 2024.https://doi.org/10.20956/j.v20i2.31632
[10] H. H. Tang and N. S. Ahmad, "Fuzzy logic approach for controlling uncertain and nonlinear systems: a comprehensive review of applications and advances," Systems Science & Control Engineering, vol. 12, p. 2394429, 2024.https://doi.org/10.1080/21642583.2024.2394429
[11] L. Kong, "Fuzzy linear regression model based on adaptive lasso method," International Journal of Fuzzy Systems, vol. 24, pp. 508-518, 2022.https://doi.org/10.1007/s40815-021-01156-0
[12] Y. Gao, Q. Wu, Z. Xu, C. Cao, J. Pan, G. Shao, et al., "Principal component analysis with fuzzy elastic net for feature selection,"IEEE Transactionson Fuzzy Systems, vol. 32,pp.6878-6890, 2024.https://doi.org/10.1109/TFUZZ.2024.3466926
[13] N. Tak and D. Inan, "Type-1 fuzzy forecasting functions with elastic net regularization," Expert Systems with Applications, vol. 199, p. 116916, 2022.https://doi.org/10.1016/j.eswa.2022.116916
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Israa Khamees Ahmed , Osamah Basheer Shukur
This work is licensed under a Creative Commons Attribution 4.0 International License.
The journal of Administration and Economics is an open- access journal that all contents are free of charge. Articles of this journal are licensed under the terms of the Creative Commons Attribution International Public License CC-BY 4.0 (https://creativecommons.org/licenses/by/4.0/legalcode) that licensees are unrestrictly allowedto search, download, share, distribute, print, or link to the full text of the articles, crawl them for indexing and reproduce any medium of the articles provided that they give the author(s) proper credits (citation). The journal allows the author(s) to retain the copyright of their published article.
Creative Commons-Attribution (BY) 
