경기종합지수 단기 예측모형 구축 연구

Abstract

After the declaration of the ‘Endemic,’ it is crucial to understand the cyclical fluctuations and turning points in the overall economic status of the nation. To promptly and accurately grasp the magnitude and speed of these economic changes, a short-term forecasting analysis was conducted using the composite index of business indicators data from January 2003 to April 2023. In this study, a two-step analysis was performed to propose a standardized forecasting process.
 Firstly, for short-term forecasting of the composite index of business indicators, the results of time series analyses considering only endogenous variables were compared with those incorporating exogenous variables. Subsequently, the study explored machine learning methods, which have been actively discussed recently, and compared and analyzed their prediction performance with traditional time series analysis methods. The analysis revealed that the addition of exogenous variables in time series analysis resulted in higher prediction accuracy compared to analyses considering only endogenous variables. Furthermore, machine learning models exhibited superior prediction performance compared to traditional time series models. As a result of the analysis with the Time-GAN model, which has the best predictive performance, it was found that there would be a rise for 1-2 months due to the growth of private consumption caused by the endemic, but it would decrease again in the 3rd month.