한국건설관리학회논문집 (Korean Journal of Construction Engineering and Management)

  • 제21권6호
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  • Pages.38-45
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  • 2020
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  • 2005-6095(pISSN)
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  • 2465-9703(eISSN)
한국건설관리학회 (Korea Institute of Construction Engineering and Management)
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양방향 LSTM과 데이터 조합탐색 및 딥러닝 관련 기법을 활용한 철근 가격 단기예측에 관한 실험적 연구

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

  • 이용성 (건국대학교 일반대학원 건축학과) ;
  • 김경환 (건국대학교 건축학부)
  • 투고 : 2020.08.24
  • 심사 : 2020.10.19
  • 발행 : 2020.11.30
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초록

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.

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.

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