A Critical Analytical Review of Hybrid Grey Models and Their Applications in Forecasting Non-Stationary Time Series - Kazem Naji, Ali Abdul Karim (Journal of Al-Qadisiyah for Computer Science and Mathematics, 2026)
Even so, forecasting non-stationary time series remains challenging due to the unavailability of data, uncertainties, and highly time-variable patterns. Due to the unique advantages of grey system theory, such as the low-data requirement, hybrid grey models have gained more and more extensive attention as hybrid models, which are realized by merging grey system theory with other intelligent methods, including fuzzy logic, artificial neural networks, met heuristic optimization, and deep learning, and have displayed significant flexibility and accuracy. This study provides a systematic and critical review of hybrid grey models, encompassing their structural framework, integration strategies, and applications in various forecasting domains. The nested, parallel, and serial hybridization approaches are then categorized, and the trade-offs between model sophistication and predictive performance are discussed. The significant drawbacks, including interpretability issues, the risks of over fitting, and the lack of standard benchmarking protocols, are also highlighted. Ultimately, the paper outlines potential research paths, including explainable AI, probabilistic reasoning, and automated mechanisms for model selection. The provided insights serve as a valuable reference for researchers and practitioners seeking to design hybrid grey forecasting models that are effective in uncertain environments.