| Title |
Experimental Study on the Short-Term Prediction of Rebar Price using Bidirectional LSTM with Data Combination and Deep Learning Related Techniques |
| Authors |
Lee, Yong-Seong ; Kim, Kyung-Hwan |
| DOI |
http://dx.doi.org/10.6106/KJCEM.2020.21.6.038 |
| Keywords |
Rebar Price; Bidirectional LSTM; Data Combination; Hyperparameter Random Search; Price Prediction; Dropout |
| Abstract |
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. |