DOI QR코드

DOI QR Code

Experimental Study on the Short-Term Prediction of Rebar Price using Bidirectional LSTM with Data Combination and Deep Learning Related Techniques

양방향 LSTM과 데이터 조합탐색 및 딥러닝 관련 기법을 활용한 철근 가격 단기예측에 관한 실험적 연구

  • 이용성 (건국대학교 일반대학원 건축학과) ;
  • 김경환 (건국대학교 건축학부)
  • Received : 2020.08.24
  • Accepted : 2020.10.19
  • Published : 2020.11.30

Abstract

This study presents a systematic procedure for developing a short-term prediction deep learning model of rebar price using bidirectional LSTM, Random Search, data combination, Dropout. In general, users intuitively determine these values, making it time-consuming and repetitive attempts to explore results with good predictive performance, and the results found by these attempts cannot be guaranteed to be excellent. With the proposed approach presented in this study, the average accuracy of short-term price forecasts is approximately 98.32%. In addition, this approach could be used as basic data to produce good predictive results in a study that predicts prices with time series data based on statistics, including building materials other than rebars.

본 연구는 양방향 LSTM, Random Search, 데이터 조합, Dropout을 이용한 철근 가격 단기예측 딥러닝 모델을 개발하는 체계적인 절차를 제시한다. 일반적으로 사용자가 직관적으로 이러한 값을 결정하여 예측성능이 우수한 결과를 탐색하는데 시간이 많이 걸리고 반복적인 시도를 하게 되는데, 이러한 시도로 찾아낸 결과가 우수하다고 보장할 수 없다. 본 연구에서 제시하는 제안된 접근방식으로 단기 가격예측의 평균 정확도는 약 98.32%이다. 그리고 이 방식은 철근 이외의 건축재료를 비롯하여 통계기반의 시계열 데이터로 가격을 예측하는 연구에서 본 연구에서 제시한 내용이 우수한 예측결과를 도출하기 위한 기초적 자료로 활용될 수 있을 것이다.

Keywords

References

  1. Bergstra, J., and Bengio, Y. (2012). "Random search for hyper-parameter optimization." The Journal of Machine Learning Research, 13(1), pp. 281-305.
  2. Cen, Z., and Wang, J. (2019). "Crude oil price prediction model with long short term memory deep learning based on prior knowledge data transfer." Energy, 169, pp. 160-171. https://doi.org/10.1016/j.energy.2018.12.016
  3. Chen, X., Wei, L., and Xu, J. (2017). "House price prediction using LSTM." arXiv preprint arXiv:1709.08432.
  4. Choi, M., and Kwon, O. (2008). "Construction material cost increase and countermeasures." Construction trend briefing by Korea Institute of Construction Industry, Vol. 2008 No.6, pp. 2-34.
  5. Choi, Y., Yim, H., and Park, B. (2009). "Analysis on the Lotting Price Fluctuation of the Multi-Family Attached House According to the Construction Material Cost Variation." Journal of The Korean Society of Civil Engineers, 29(6D), pp. 753-760.
  6. Claesen, M., and De Moor, B. (2015). "Hyperparameter search in machine learning." arXiv preprint arXiv:1502.02127.
  7. Gao, X., Shi, M., Song, X., Zhang, C., and Zhang, H. (2019). "Recurrent neural networks for real-time prediction of TBM operating parameters." Automation in Construction, 98, pp. 225-235. https://doi.org/10.1016/j.autcon.2018.11.013
  8. Hochreiter, S., and Schmidhuber, J. (1997). "Long shortterm memory." Neural computation, 9(8), pp. 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
  9. Jang, Y., Jeong, I., and Cho, Y. (2020). "Business Failure Prediction of Construction Contractors Using a LSTM RNN with Accounting, Construction Market, and Macroeconomic Variables." Journal of Management in Engineering, 36(2), 04019039. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000733
  10. Jeong, D. (2017). "Trend on Artificial Intelligence Technology and Its Related Industry." Korea Institute of Information Technology Magazine, 15(2), pp. 21-28. https://doi.org/10.14801/jkiit.2017.15.5.21
  11. Joo, I., and Choi, S. (2018). "Stock prediction model based on bidirectional LSTM recurrent neural network." The Journal of Korea Institute of Information, Electronics, and Communication Technology, 11(2), pp. 204-208. https://doi.org/10.17661/JKIIECT.2018.11.2.204
  12. Lahari, M. C., Ravi, D. H., and Bharathi, R. (2018). "Fuel Price Prediction Using RNN." 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1510-1514. IEEE.
  13. Larochelle, H., Erhan, D., Courville, A., Bergstra, J., and Bengio, Y. (2007). "An empirical evaluation of deep architectures on problems with many factors of variation." Proceedings of the 24th international conference on Machine learning, pp. 473-480.
  14. Lee, J. (2019). "A Comparative Study on Stock Price Forecasting Models Using LSTM and Bidirectional Neural Networks." MS thesis, Seoul National University of Science and Technology Graduate School.
  15. Liao, T. W. (2005). "Clustering of time series data-a survey." Pattern recognition, 38(11), pp. 1857-1874. https://doi.org/10.1016/j.patcog.2005.01.025
  16. Mou, L., Zhao, P., and Chen, Y. (2019). "Short-Term Traffic Flow Prediction: A Long Short-Term Memory Model Enhanced by Temporal Information." CICTP 2019, pp. 2411-2422.
  17. Olson, D. L., and Delen, D. (2008). Advanced data mining techniques, Springer-Verlag, Berlin Heidelberg.
  18. Pawar, K., Jalem, R. S., and Tiwari, V. (2019). "Stock market price prediction using LSTM RNN." Emerging Trends in Expert Applications and Security , pp. 493-503. Springer, Singapore.
  19. Schuster, M., and Paliwal, K. K. (1997). "Bidirectional recurrent neural networks." IEEE transactions on Signal Processing, 45(11), pp. 2673-2681. https://doi.org/10.1109/78.650093
  20. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. (2014). "Dropout: a simple way to prevent neural networks from overfitting." The journal of machine learning research, 15(1), pp. 1929-1958.
  21. Trinh, T. H., Dai, A. M., Luong, M. T., and Le, Q. V. (2018). "Learning longer-term dependencies in rnns with auxiliary losses." arXiv preprint arXiv:1803.00144.
  22. Yoon, J. (2019). "Effectiveness analysis of credit card default risk prediction using deep learning neural networks." Financial Research, 33 (1), pp. 151-183.
  23. Zhang, Z., Wang, Y., Chen, P., and Yu, G. (2018). "Application of long short-term memory neural network for multistep travel time forecasting on urban expressways." CICTP 2017: Transportation Reform and Change-Equity, Inclusiveness, Sharing, and Innovation, ASCE, pp. 444-454.