참고문헌 (22)

1. Korea International Trade Association [Internet], https://stat.kita.net. 

2. B. M. Lee, H. J. Jeong, and K. S. Park, "An influence of the fourth industrial revolution on international trade and countermeasure strategies to promote export in Korea," Korea Trade Review, Vol.42, No.3, pp.1-24, 2017. 

3. S. H. Nam, "Comparison of long-term forecasting performance of export growth rate using time series analysis models and machine learning analysis," Korea Trade Review, Vol.46, No.6, pp.191-209, 2021. 

4. The 9th Public Data Utilization BI Contest [Internet], http://www.datacontest.kr (retrieved 20210926) 

5. S. Weisberg, "Applied linear regression," John Wiley & Sons, pp.47, 2005. 

6. Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature, Vol.521, No.7553, pp.436-444, 2015. 

7. A. Keck, A. Raubold, and A. Truppia. "Forecasting international trade: A time series approach," OECD Journal: Journal of Business Cycle Measurement and Analysis, Vol.2009, No.2, pp.157-176, 2010. 

   상세보기

8. A. W. Veenstra and H. E. Haralambides. "Multivariate autoregressive models for forecasting seaborne trade flows," Transportation Research Part E: Logistics and Transportation Review, Vol.37, No.4, pp.311-319, 2001. 

9. E. S. Silva and H. Hassani. "On the use of singular spectrum analysis for forecasting US trade before, during and after the 2008 recession," International Economics, Vol.141, pp.34-49, 2015. 

   상세보기

10. M. E. Torres, M. A. Colominas, G. Schlotthauer, and P. Flandrin, "A complete ensemble empirical mode decomposition with adaptive noise," 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2011. 

11. H. Lu, X. Ma, K. Huang, and M. Azimi, "Carbon trading volume and price forecasting in China using multiple machine learning models," Journal of Cleaner Production, Vol.249, pp.119386, 2020. 

    상세보기

12. S. Ioffe and C. Szegedy, "Batch normalization: Accelerating deep network training by reducing internal covariate shift," International Conference on Machine Learning, PMLR, 2015. 

13. S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural Computation, Vol.9, No.8, pp.1735-1780, 1997. 

    상세보기

14. Analytics Vidhya, "Feature Scaling for Machine Learning: Understanding the Difference Between Normalization vs. Standardization," [Internet], https://www.analyticsvidhya.com/blog/2020/04/feature-scaling-machine-learning-normalization-standardization/(retrieved 20200403). 

15. N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, "Dropout: A simple way to prevent neural networks from overfitting," The Journal of Machine Learning Research, Vol.15, No.1, pp.1929-1958, 2014. 

    상세보기

16. R. Genuer, J. M. Poggi, and C. Tuleau-Malot. "Variable selection using random forests," Pattern Recognition Letters, Vol.31, No.14, pp.2225-2236, 2010. 

    상세보기

17. Christoph Molnar, "Permutation feature importance," [Internet], https://christophm.github.io/interpretable-ml-book/feature-importance.html (retrieved 20220217). 

18. K. Kira and L. A. Rendell, "A practical approach to feature selection," Machine Learning Proceedings 1992, Morgan Kaufmann, pp.249-256, 1992. 

19. J. W. Tukey, "Exploratory data analysis," Addison-Wesley. ISBN 978-0-201-07616-5. OCLC 3058187, 1977. 

20. A. Krizhevsky, I. Sutskever, and G. E. Hinton. "Imagenet classification with deep convolutional neural networks," Communications of the ACM, Vol.60, No.6, pp.84-90, 2017. 

    상세보기

21. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," Proceedings of the IEEE, Vol.86, No.11, pp.2278-2324, 1998. 

22. A. Vaswani et al., "Attention is all you need," Advances in Neural Information Processing Systems, 2017.