An Investigation of Ensemble and Explainable Machine Learning Models for Consumer Price Index Prediction in Ethiopia: Evidence from 1990-2023

Abstract

A plenty of research investigations have demonstrated the pivotal role that the consumer price index (CPI) plays in the comprehensive assessment and understanding of inflationary trends within an economy. For prediction of consumer price index, researchers have used a variety of empirical methodologies and sophisticated statistical techniques, each of which come up with inherent challenges regarding their generalizability. To solve issues with generalizability, researchers developed artificial intelligence and machine learning. In this work, the sophisticated machine learning models such as decision tree (DT), random forest (RF), and gradient boosting (GB)s have integrated together to develop super learner machine learning to enhance the predictive capabilities concerning the consumer price index robustly. To ensure the optimization of these models, rigorous methodologies such as K-fold cross-validation and an extensive grid search for hyperparameter tuning are meticulously applied to refine the model's performance. The efficiency and predictive performance of the proposed super learner model are then critically compared with those of other base models, thereby establishing a benchmark for assessing improvements in predictive accuracy. Remarkably, the suggested super learner model demonstrated superior prediction capabilities, achieving highest coefficient of determination (R 2 ) of 98.3%, with the lowest mean absolute error of 2.89%, a mean absolute percentage error of 2.60%, and a root mean square error of 3.46%. Moreover, the interpretability of the model's predictions is significantly explained through the application of Shapley additive explanations. This method explains the essential factors that influence the consumer price index in the context of Ethiopia. The super learner model that has been developed possesses the versatility and accuracy necessary for application across various economic sectors, enabling researchers and practitioners to predict dependent variables with both precision and efficiency, thus facilitating informed decision-making in financial planning.