| Title |
Experimental Study on Long-Term Prediction of Rebar Price Using Deep Learning Recursive Prediction Meothod |
| Authors |
Lee, Yong-Seong ; Kim, Kyung-Hwan |
| DOI |
https://dx.doi.org/10.6106/KJCEM.2021.22.3.021 |
| Keywords |
Recursive prediction method; Long term prediction; Rebar price prediction |
| Abstract |
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. |