Korea Trade Review (무역학회지)

Korea Trade Research Association (한국무역학회)
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Machine Learning and Deep Learning Models to Predict Income and Employment with Busan's Strategic Industry and Export

머신러닝과 딥러닝 기법을 이용한 부산 전략산업과 수출에 의한 고용과 소득 예측

  • Chae-Deug Yi (Department of International Trade, Pusan National University)
  • Received : 2021.02.01
  • Accepted : 2021.02.26
  • Published : 2021.02.28
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Abstract

This paper analyzes the feasibility of using machine learning and deep learning methods to forecast the income and employment using the strategic industries as well as investment, export, and exchange rates. The decision tree, artificial neural network, support vector machine, and deep learning models were used to forecast the income and employment in Busan. The following were the main findings of the comparison of their predictive abilities. First, the decision tree models predict the income and employment well. The forecasting values for the income and employment appeared somewhat differently according to the depth of decision trees and several conditions of strategic industries as well as investment, export, and exchange rates. Second, since the artificial neural network models show that the coefficients are somewhat low and RMSE are somewhat high, these models are not good forecasting the income and employment. Third, the support vector machine models show the high predictive power with the high coefficients of determination and low RMSE. Fourth, the deep neural network models show the higher predictive power with appropriate epochs and batch sizes. Thus, since the machine learning and deep learning models can predict the employment well, we need to adopt the machine learning and deep learning models to forecast the income and employment.

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