Artificial intelligence and its impact on the prediction of economic indicators

Abstract

Economic indicators are key statistics based on economy, some examples of economic indicators are inflation rate, gross domestic product (GDP), unemployment rate, consumer price indices (CPI), interest rate, exports, consumption of energy, among others. Most of the published studies are focused on contextualizing and predict a particular economic indicator without considering the current general situation on how non-linear models have been used in predicting some of the economic indicators. This article, has analyzed in the scientific production the artificial intelligence methods mostly used in the development of prediction models of economic indicators. The study was carried out by means of a systematic literature review (SLR) using the Web of Science (WOS), Scopus and Google Scholar bibliographic databases (BD) as resources. The documents and general information analyzed qualitatively are filtered between the range of years 2015 to 2019 to which an adequate set of quality and selection criteria were applied. The approach of the research questions allowed to describe the outcomes in categories where the studies by predicted economic indicator and applied artificial intelligence method have been successfully included. The outcomes that have been obtained in this article represent a starting point for researchers, academics and professionals who wish to carry out studies related to the prediction of economic indicators using some artificial intelligence (AI) methods. In conclusion, some of the artificial intelligence methods used to predict economic indicators are artificial neural networks (ANN), adaptive systems of diffuse neuro inference (ANFIS), genetic programming (GP), support vector regression (SVR), machines extreme learning and other machine learning (ML) techniques.