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Goldani, mahdi. (1403). Evaluating Feature Selection Methods for Macro-Economic Forecasting, Applied for Iran’s macro-economic variables. , (), -. doi: 10.22059/jsciences.2025.383754.1007888
mahdi Goldani. "Evaluating Feature Selection Methods for Macro-Economic Forecasting, Applied for Iran’s macro-economic variables". , , , 1403, -. doi: 10.22059/jsciences.2025.383754.1007888
Goldani, mahdi. (1403). 'Evaluating Feature Selection Methods for Macro-Economic Forecasting, Applied for Iran’s macro-economic variables', , (), pp. -. doi: 10.22059/jsciences.2025.383754.1007888
Goldani, mahdi. Evaluating Feature Selection Methods for Macro-Economic Forecasting, Applied for Iran’s macro-economic variables. , 1403; (): -. doi: 10.22059/jsciences.2025.383754.1007888

Evaluating Feature Selection Methods for Macro-Economic Forecasting, Applied for Iran’s macro-economic variables

Journal of Sciences, Islamic Republic of Iran
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 28 بهمن 1403
نوع مقاله: Original Paper
شناسه دیجیتال (DOI): 10.22059/jsciences.2025.383754.1007888
نویسنده
mahdi Goldani*
Hakim Sabzevari University, Sabzevar, Iran
چکیده
This research examines different feature selection methods aimed at enhancing the predictive accuracy of macroeconomic forecasting models, focusing on Iran’s economic indicators derived from World Bank data. Fourteen feature selection techniques, classified into Filter, Wrapper, Embedded, and Similarity-based categories, were thoroughly compared. The evaluation utilized Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) metrics under a 10-fold cross-validation scheme. The findings highlight that Stepwise Selection, Tree-based approaches, and Similarity-based methods, especially those employing Hausdorff and Euclidean distances, consistently outperformed others with average MAE values of 32.03 for Stepwise Selection and 62.69 for Hausdorff Distance. Conversely, Recursive Feature Elimination and Variance Thresholding exhibited weaker results, yielding significantly higher average MAE scores. Similarity-based approaches achieved an average rank of 9.125 across datasets, demonstrating their robustness in managing high-dimensional macroeconomic data. These outcomes underscore the value of integrating similarity measures with traditional feature selection techniques to improve the efficiency and reliability of predictive models, offering meaningful insights for researchers and policymakers in economic forecasting.
کلیدواژه‌ها
Feature Selection؛ Predictive Accuracy؛ World Bank Indicators؛ Macroeconomic Analysis؛ Similarity Methods
آمار
تعداد مشاهده مقاله: 124





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