Deep Index Price Forecasting in Steel Industry
Conference proceedings article
Authors/Editors
Strategic Research Themes
Publication Details
Author list: Ganokratanaa T., Ketcham M.
Publisher: Elsevier
Publication year: 2021
ISBN: 9781665438315
ISSN: 0928-4931
eISSN: 1873-0191
Languages: English-Great Britain (EN-GB)
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Abstract
Steel is one of the most expensive materials in the construction industry. Currently, Thailand imports steel from abroad, facing a price fluctuation due to the economy, production capacity, and consumption in domestic and international markets. The cost control of the steel price can also be unstable and risky to purchase. To handle these issues, there is a need for good management of the quantity and procurement of steel at the right price. Thus, we propose a prediction of the steel price index in construction using deep learning neuron networks. Our experimental results show good performance as our mean square error equals 2.34. Our proposed method can be applied for decision-making support and used as a reliable system for steel purchases in construction projects. © 2021 IEEE.
Keywords
neuron network, steel price
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