| Title |
A Study on the Application of the Price Prediction of Construction Materials through the Improvement of Data Refactor Techniques |
| Authors |
Lee, Woo-Yang ; Lee, Dong-Eun ; Kim, Byung-Soo |
| DOI |
https://dx.doi.org/10.6106/KJCEM.2023.24.6.066 |
| Keywords |
Construction Raw Materials; Ensemble Economic Mode Decomposition; Data Refactoring Techniques; Data Reconstruction; Price Prediction |
| Abstract |
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. |