[논문]딥러닝의 반복적 예측방법을 활용한 철근 가격 장기예측에 관한 실험적 연구

이용성; 김경환

doi:10.6106/kjcem.2021.22.3.021

딥러닝의 반복적 예측방법을 활용한 철근 가격 장기예측에 관한 실험적 연구
Experimental Study on Long-Term Prediction of Rebar Price Using Deep Learning Recursive Prediction Meothod 원문보기

한국건설관리학회논문집 = Korean journal of construction engineering and management, v.22 no.3, 2021년, pp.21 - 30

이용성 (건국대학교 일반대학원 건축학과) , 김경환 (건국대학교 건축공학부)

초록
AI-Helper

본 연구는 딥러닝의 반복적 예측방식을 활용하여 5개월의 철근 가격 예측방법을 제안한다. 이 방식은 입력데이터의 특성을 모두 단기예측하여 원 데이터에 추가하고, 추가된 데이터로 다음의 시점을 예측하는 과정을 반복하여 장기 예측한다. 본 연구에서 제시하는 방식으로 1개월에서 5개월까지 예측한 철근 가격의 예측 평균 정확도는 약 97.24%이다. 제안된 방식을 통해 인간의 경험과 판단을 통한 가격 추정방법의 체계성을 보완하여 기존의 방식보다 정확한 비용계획이 가능할 것으로 사료된다. 또 철근 이외의 건축재료를 비롯하여 시계열 데이터로 가격을 장기예측하는 연구에서 본 연구에서 제시한 방법이 활용될 수 있을 것으로 기대한다.

Abstract ▼ AI-Helper

This study proposes a 5-month rebar price prediction method using the recursive prediction method of deep learning. This approach predicts a long-term point in time by repeating the process of predicting all the characteristics of the input data and adding them to the original data and predicting the next point in time. The predicted average accuracy of the rebar prices for one to five months is approximately 97.24% in the manner presented in this study. Through the proposed method, it is expected that more accurate cost planning will be possible than the existing method by supplementing the systematicity of the price estimation method through human experience and judgment. In addition, it is expected that the method presented in this study can be utilized in studies that predict long-term prices using time series data including building materials other than rebar.

주제어

표/그림 (11)

그림 Fig. 1. Input data and prediction output data by prediction time (Choi and Lee, 2020)
표 Table 1. System configuration specifications and package information
표 Table 2. Hyper parameter and Description
표 Table 3. Data information
그림 Fig. 3. Data processing process for short-term prediction of the recurrent neural network
그림 Fig. 2. Visualization of Pearson correlation coefficient analysis results
그림 Fig. 4. Prediction method of rebar price using recursive Short-Term Forecasting Method
표 Table 4. First experiment result (Pred, 2020.4.)
표 Table 5. Second experiment result (2020.4.~2020.9.)
표 Table 6. Predicted result of the second experiment
그림 Fig. 5. Visualization to compare actual values with predicted values

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