1
Computer Science Department, Faculty of Computers and Information, Suez University, Suez 43512, Egypt
(elshewy86@gmail.com)
Abstract :
Inflation, an omnipresent economic phenomenon, is marked by a continual upsurge in the overall price levels of commodities and services within an economy. Accurately predicting inflation within a data-abundant setting poses a formidable challenge and remains a dynamic area of research encompassing several unresolved methodological inquiries. Among these, a significant query pertains to the identification and extraction of data offering the highest predictive capability for a targeted variable, particularly in scenarios characterized by numerous closely interconnected predictors, as encountered in the context of inflation prediction. Recently, the application of machine learning (ML) models has gained traction in predicting inflation parameters. The predictive accuracy of such models hinges significantly on the selection of an appropriate framework. Ensemble models, designed to amalgamate multiple base models, have emerged as a compelling strategy to yield superior predictive outcomes. In this study, we introduce a novel weighted average ensemble model tailored for the prognostication of inflation prediction. The proposed approach leverages three foundational base models: Linear Regression (LR), Polynomial Regression (PR), and Moving Average (MA) regression. The critical aspect of this ensemble lies in optimizing the weights assigned to each base model, thereby accentuating their individual strengths. To achieve this, we employ the Waterwheel Plant Optimization Algorithm (WWPA), a proficient optimization algorithm, to discern the optimal weight distribution for the base models. Comparative evaluations are conducted, pitting the proposed model against three another base models. Empirical findings conclusively demonstrate the superiority of the proposed weighted average ensemble model, underscoring its capacity to predict inflation with exceptional efficiency.
Keywords :
Inflation; machine learning; linear regression; polynomial regression; moving average regression; ensemble model.
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| Style | # |
|---|---|
| MLA | Ahmed M. Elshewey. "An Optimized Ensemble Model for Inflation Prediction in Egypt." American Journal of Business and Operations Research, Vol. 10, No. 2, 2023 ,PP. 61-73 (Doi : https://doi.org/10.54216/AJBOR.100207) |
| APA | Ahmed M. Elshewey. (2023). An Optimized Ensemble Model for Inflation Prediction in Egypt. Journal of American Journal of Business and Operations Research, 10 ( 2 ), 61-73 (Doi : https://doi.org/10.54216/AJBOR.100207) |
| Chicago | Ahmed M. Elshewey. "An Optimized Ensemble Model for Inflation Prediction in Egypt." Journal of American Journal of Business and Operations Research, 10 no. 2 (2023): 61-73 (Doi : https://doi.org/10.54216/AJBOR.100207) |
| Harvard | Ahmed M. Elshewey. (2023). An Optimized Ensemble Model for Inflation Prediction in Egypt. Journal of American Journal of Business and Operations Research, 10 ( 2 ), 61-73 (Doi : https://doi.org/10.54216/AJBOR.100207) |
| Vancouver | Ahmed M. Elshewey. An Optimized Ensemble Model for Inflation Prediction in Egypt. Journal of American Journal of Business and Operations Research, (2023); 10 ( 2 ): 61-73 (Doi : https://doi.org/10.54216/AJBOR.100207) |
| IEEE | Ahmed M. Elshewey, An Optimized Ensemble Model for Inflation Prediction in Egypt, Journal of American Journal of Business and Operations Research, Vol. 10 , No. 2 , (2023) : 61-73 (Doi : https://doi.org/10.54216/AJBOR.100207) |