The fluctuation of the rebar price is hard to predict due to too many factors involved within the production of the steel industry. Current models and database for predicting the rebar price mainly focus on the economic performance, which is not suitable for construction industry. Along with the rapid application of the artificial intelligence technology, the deep learning concept is widely used in developing algorithms for predicting model. Convolutional neural network, the class of the deep learning, is good at extracting features from multidimensional data. Therefore, this research develops a convolutional neural network to predict the fluctuation of the rebar price. First, the factors including domestic and foreign events that could impact the rebar price are analyzed. Then the data of the above-mentioned factors are transformed into picture to train the proposed prediction model for rebar price. Finally, keywords that represent different events impact rebar price are used to calibrate the accuracy of the proposed model.
|Translated title of the contribution||Hybridizing Deep Learning with Google Trends to Predict Rebar Price Fluctuation in Taiwan|
|Original language||Chinese (Traditional)|
|Number of pages||10|
|Journal||Journal of the Chinese Institute of Civil and Hydraulic Engineering|
|Publication status||Published - 2021 Dec|
All Science Journal Classification (ASJC) codes
- Civil and Structural Engineering