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      Data Refactor 기법의 개선을 통한 건설원자재 가격 예측 적용성 연구 = A Study on the Application of the Price Prediction of Construction Materials through the Improvement of Data Refactor Techniques

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      https://www.riss.kr/link?id=A108846600

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      The construction industry suffers losses due to failures in demand forecasting due to price fluctuations in construction raw materials, increased user costs due to project cost changes, and lack of forecasting system. Accordingly, it is necessary to improve the accuracy of construction raw material price forecasting. This study aims to predict the price of construction raw materials and verify applicability through the improvement of the Data Refactor technique. In order to improve the accuracy of price prediction of construction raw materials, the existing data refactor classification of low and high frequency and ARIMAX utilization method was improved to frequency-oriented and ARIMA method utilization, so that short-term (3 months in the future) six items such as construction raw materials lumber and cement were improved. ), mid-term (6 months in the future), and long-term (12 months in the future) price forecasts. As a result of the analysis, the predicted value based on the improved Data Refactor technique reduced the error and expanded the variability. Therefore, it is expected that the budget can be managed effectively by predicting the price of construction raw materials more accurately through the Data Refactor technique proposed in this study.
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      The construction industry suffers losses due to failures in demand forecasting due to price fluctuations in construction raw materials, increased user costs due to project cost changes, and lack of forecasting system. Accordingly, it is necessary to i...

      The construction industry suffers losses due to failures in demand forecasting due to price fluctuations in construction raw materials, increased user costs due to project cost changes, and lack of forecasting system. Accordingly, it is necessary to improve the accuracy of construction raw material price forecasting. This study aims to predict the price of construction raw materials and verify applicability through the improvement of the Data Refactor technique. In order to improve the accuracy of price prediction of construction raw materials, the existing data refactor classification of low and high frequency and ARIMAX utilization method was improved to frequency-oriented and ARIMA method utilization, so that short-term (3 months in the future) six items such as construction raw materials lumber and cement were improved. ), mid-term (6 months in the future), and long-term (12 months in the future) price forecasts. As a result of the analysis, the predicted value based on the improved Data Refactor technique reduced the error and expanded the variability. Therefore, it is expected that the budget can be managed effectively by predicting the price of construction raw materials more accurately through the Data Refactor technique proposed in this study.

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      참고문헌 (Reference)

      1 지세현 ; 박문서 ; 이현수 ; 윤유상, "건설공사 공사비 예측 및 관리기술 발전방향 : 호주 사례를 중심으로" 한국건설관리학회 9 (9): 168-179, 2008

      2 이재명 ; 유정호 ; 김창덕 ; 이광재 ; 임병수, "건설 현장 자재수요 변동을 고려한 주문시점 산정 방법" 대한건축학회 24 (24): 117-125, 2008

      3 "U.S. Energy Information Administration"

      4 "U.S. Bureau of Labor Statistics"

      5 Lee, Y.S., "Long and Short Term Prediction of Rebar Price Using Deep learning and Related Techniques" Konkuk University 2022

      6 Lean Yu, "Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm" 30 (30): 2623-2635, 2008

      7 Wu, Zhaohua, "Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method" 1 : 1-41, 2009

      8 P. Flandrin, "Empirical mode decomposition as a filter bank" 11 (11): 112-114, 2004

      9 Lee, G. B., "Data-Driven Signal Decomposition using Improved Ensemble EMD Method" 19 (19): 279-286, 2015

      10 Park, S. W., "Constraint Factors for Construction Investment Recovery: Causes and Effects of Soaring Construction Material Prices" Bank of Korea 2022

      1 지세현 ; 박문서 ; 이현수 ; 윤유상, "건설공사 공사비 예측 및 관리기술 발전방향 : 호주 사례를 중심으로" 한국건설관리학회 9 (9): 168-179, 2008

      2 이재명 ; 유정호 ; 김창덕 ; 이광재 ; 임병수, "건설 현장 자재수요 변동을 고려한 주문시점 산정 방법" 대한건축학회 24 (24): 117-125, 2008

      3 "U.S. Energy Information Administration"

      4 "U.S. Bureau of Labor Statistics"

      5 Lee, Y.S., "Long and Short Term Prediction of Rebar Price Using Deep learning and Related Techniques" Konkuk University 2022

      6 Lean Yu, "Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm" 30 (30): 2623-2635, 2008

      7 Wu, Zhaohua, "Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method" 1 : 1-41, 2009

      8 P. Flandrin, "Empirical mode decomposition as a filter bank" 11 (11): 112-114, 2004

      9 Lee, G. B., "Data-Driven Signal Decomposition using Improved Ensemble EMD Method" 19 (19): 279-286, 2015

      10 Park, S. W., "Constraint Factors for Construction Investment Recovery: Causes and Effects of Soaring Construction Material Prices" Bank of Korea 2022

      11 손건희 ; 김기웅 ; 신리현 ; 이수미, "ARIMA 시계열 모형을 이용한 제주도 인바운드 항공여객 증가율 예측 연구 - 제주지역 골프장 내장객 현황 데이터를 활용하여 -" 한국항공운항학회 31 (31): 92-98, 2023

      12 Xun Zhang, "A new approach for crude oil price analysis based on Empirical Mode Decomposition" 30 (30): 905-918, 2008

      13 Hao Du, "A New Approach for Reconstruction of IMFs of Decomposition and Ensemble Model for Forecasting Crude Oil Prices" 2020 : 1325071-, 2020

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