2692-2967ISSN (Online)
2770-0216ISSN (Print)
Volume 10 , Issue 2 , PP: 61-73, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Ahmed M. Elshewey 1 *
Doi: https://doi.org/10.54216/AJBOR.100207
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.
Inflation , machine learning , linear regression , polynomial regression , moving average regression , ensemble model.
[1] Di Vaio A, Hassan R., Alavoine C, Data intelligence and analytics: A bibliometric analysis of human–Artificial intelligence in public sector decision-making effectiveness. Technological Forecasting and Social Change, 174, 121201, 2022.
[2] Araujo, G S Gaglianone, W P, Machine learning methods for inflation forecasting in Brazil: New contenders versus classical models. Latin American Journal of Central Banking, 4(2), 2023.
[3] Chavarría J D, Morales C C, Pronósticos de inflación mediante técnicas bayesianas. Economía y Sociedad, 20(48), 1-27, 2015.
[4] Muñoz Salas E, El modelo macroeconómico de proyección trimestral del Banco Central de Costa Rica. Modelos macroeconométricos de la banca central: Centroamérica y República Dominicana. México, DF: CEPAL, 16-43, 2008.
[5] Aras, S. and Lisboa, P.J., Explainable inflation forecasts by machine learning models. Expert Systems with Applications, 207, 117982, 2022.
[6] Aranda R A, Camacho M, Pérez-Quirós G, Finite sample performance of small versus large scale dynamic factor models. Documentos de trabajo del Banco de España, (4), 1-53, 2012.
[7] Boivin, J. and Ng, S., Are more data always better for factor analysis?. Journal of Econometrics, 132(1), 169-194, 2006.
[8] D’Agostino, A. and Giannone, D., Comparing alternative predictors based on large‐panel factor models. Oxford bulletin of economics and statistics, 74(2), 306-326, 2012.
[9] Konzen, E. and Ziegelmann, F.A., LASSO‐Type Penalties for Covariate Selection and Forecasting in Time Series. Journal of Forecasting, 35(7), 592-612, 2016.
[10] Medeiros, M.C. and Mendes, E.F., ℓ1-regularization of high-dimensional time-series models with non-Gaussian and heteroskedastic errors. Journal of Econometrics, 191(1), 255-271, 2016.
[11] Bai, J. and Ng, S., Forecasting economic time series using targeted predictors. Journal of Econometrics, 146(2), 304-317, 2008.
[12] Kim, H.H. and Swanson, N.R., Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods. International Journal of Forecasting, 34(2), 339-354, 2018.
[13] Ahmed, N.K., Atiya, A.F., Gayar, N.E. and El-Shishiny, H., An empirical comparison of machine learning models for time series forecasting. Econometric reviews, 29(5-6), 594-621, 2010.
[14] Masini, R.P., Medeiros, M.C. and Mendes, E.F., Machine learning advances for time series forecasting. Journal of economic surveys, 37(1), 76-111, 2023.
[15] Breiman, L., Random forests. Machine learning, 45, 5-32, 2001.
[16] Biau, O. and D’Elia, A., 2009. Euro area GDP forecasting using large survey datasets. In A random forest approach, 2009.
[17] David, B., Model economic phenomena with CART and Random Forest algorithms (No. 2017-46). University of Paris Nanterre, EconomiX, 2017.
[18] Bajari, P., Nekipelov, D., Ryan, S.P. and Yang, M., Machine learning methods for demand estimation. American Economic Review, 105(5), 481-485, 2015.
[19] Rodríguez-Vargas, A., Forecasting Costa Rican inflation with machine learning methods. Latin American Journal of Central Banking, 1(1-4), 100012, 2020.
[20] Yang, C. and Guo, S., Inflation prediction method based on deep learning. Computational Intelligence and Neuroscience, 2021.
[21] Simionescu, M., Econometrics of sentiments-sentometrics and machine learning: The improvement of inflation predictions in Romania using sentiment analysis. Technological Forecasting and Social Change, 182, 121867, 2022.
[22] Theoharidis, A.F., Guillén, D.A. and Lopes, H., Deep learning models for inflation forecasting. Applied Stochastic Models in Business and Industry, 2023.
[23] Alkhammash, E.H., Hadjouni, M. and Elshewey, A.M., A hybrid ensemble stacking model for gender voice recognition approach. Electronics, 11(11), 1750, 2022.
[24] Shams, M.Y., El-kenawy, E.S.M., Ibrahim, A. and Elshewey, A.M., A hybrid dipper throated optimization algorithm and particle swarm optimization (DTPSO) model for hepatocellular carcinoma (HCC) prediction. Biomedical Signal Processing and Control, 85, 104908, 2023.
[25] Benjamin, M.A., Rigby, R.A. and Stasinopoulos, D.M., Generalized autoregressive moving average models. Journal of the American Statistical association, 98(461), 214-223, 2023.
[26] Rocha, A.V. and Cribari-Neto, F., Beta autoregressive moving average models. Test, 18, 529-545, 2009.
[27] Petukhova, T., Ojkic, D., McEwen, B., Deardon, R. and Poljak, Z., Assessment of autoregressive integrated moving average (ARIMA), generalized linear autoregressive moving average (GLARMA), and random forest (RF) time series regression models for predicting influenza A virus frequency in swine in Ontario, Canada. PloS one, 13(6), e0198313, 2018.
[28] Ostertagová, E., Modelling using polynomial regression. Procedia Engineering, 48, 500-506, 2012.
[29] Cheng, C.L. and Schneeweiss, H., Polynomial regression with errors in the variables. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 60(1), 189-199, 1998.
[30] Bickel, P.J. and Li, B., Local polynomial regression on unknown manifolds. Lecture Notes-Monograph Series, 177-186, 2007.
