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http://dx.doi.org/10.15207/JKCS.2020.11.11.257

Prediction of the employment ratio by industry using constrainted forecast combination  

Kim, Jeong-Woo (Department of Economics, Gangneung Wonju National University)
Publication Information
Journal of the Korea Convergence Society / v.11, no.11, 2020 , pp. 257-267 More about this Journal
Abstract
In this study, we predicted the employment ratio by the export industry using various machine learning methods and verified whether the prediction performance is improved by applying the constrained forecast combination method to these predicted values. In particular, the constrained forecast combination method is known to improve the prediction accuracy and stability by imposing the sum of predicted values' weights up to one. In addition, this study considered various variables affecting the employment ratio of each industry, and so we adopted recursive feature elimination method that allows efficient use of machine learning methods. As a result, the constrained forecast combination showed more accurate prediction performance than the predicted values of the machine learning methods, and in particular, the stability of the prediction performance of the constrained forecast combination was higher than that of other machine learning methods.
Keywords
Machine learning; Prediction; Forecasting combination; Regularization; Recursive feature elimination;
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Times Cited By KSCI : 4  (Citation Analysis)
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