| Title |
Long-term Prediction of Rebar Price Using Bidirectional Long Short-Term Memory and Time Series Cross-Validation |
| Authors |
이용성(Lee, Yong-Seong) ; 김경환(Kim, Kyung-Hwan) |
| DOI |
https://doi.org/10.5659/JAIK.2022.38.5.269 |
| Keywords |
Bidirectional LSTM; Time Series Cross-Validation; Recursive Prediction Method; Rebar Price Prediction |
| Abstract |
This study proposes a long-term prediction method of rebar price using deep learning techniques such as a bidirectional long and short-term
memory (Bi-LSTM), a recursive method, and a time series cross-validation. Among recurrent neural network (RNN) models, Bi-LSTM
provides the best prediction performance for small time series data such as monthly rebar price when applied. The recursive method uses the
short-term prediction result as an input value for predicting the next time point data, which can repeatedly be used for making long-term
predictions. Time-series cross-validation enables more stable prediction accuracy by enhancing learning that may be lacking in small time
series data. By applying these deep learning techniques, this study predicts the monthly rebar price for up to 5 months and compares it with
the previous study. As a result, it has been found that the average accuracy increases, and the deviation of the predicted values decreases. |