Volume 6 • Issue 1 • PP: 01–14 • 2026
DTOSFS–CatBoost: A Hybrid Metaheuristic Framework for Accurate and Interpretable Unemployment Forecasting
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The fact that educational, demographic, and macroeconomic variables interact nonlinearly has remained a thorn in the flesh of socio-economic analytics to date, making it challenging to forecast unemployment with sufficient precision. To address this, the current study presents a hybrid metaheuristic, Dipper Throated Optimization with Stochastic Fractal Search (DTOSFS), coupled with the Category Boosting (CatBoost) algorithm to improve predictive modelling. The suggested DTOSFS-CatBoost system combines the general exploratory search of DTO with SFS refinement to stochastic local optimization of hyperparameters, and alleviates overfitting. Empirical experiments have shown that whereas the original CatBoost gave results with a Mean Squared Error (MSE) of 0.0256 and Root Mean Squared Error (RMSE) of 0.1601 with a correlation coefficient of 0.873, the CatBoost optimized by DTOSFS had drastically better results with an MSE of 0.00033, RMSE of 0.00207, and a correlation coefficient of 0.930. These results confirm an increased exploration-to-exploitation ratio in DTOSFS and yield small, powerful designs that substantially enhance model stability, precision, and convergence speed. These results show that educational attainment (at least tertiary and primary enrollment) and demographics (at least the birth rate) are influential factors in unemployment variation. This addition to predictive performance is not the only one, and it provides a predictive data-driven labor-market optimization paradigm that can be replicated and interpreted. The research observes that hybrid metaheuristics and gradient boosting can be used to drive next generation economic intelligence systems for adaptive policy formulation and to enhance online, privacy conscious, and cross-domain unemployment prediction.
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