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https://hdl.handle.net/10316/114907
Title: | Forecasting Steel Production in the World—Assessments Based on Shallow and Deep Neural Networks | Authors: | Mateus, Balduíno César Mendes, Mateus Farinha, José Torres Cardoso, António J. Marques Assis, Rui Costa, Lucélio M. da |
Keywords: | steel production; time series; neural networks; CNN; MLP; LSTM; GRU | Issue Date: | 2022 | Publisher: | MDPI | Project: | COFAC/EIGeS. | Serial title, monograph or event: | Applied Sciences (Switzerland) | Volume: | 13 | Issue: | 1 | Abstract: | Forecasting algorithms have been used to support decision making in companies, and it is necessary to apply approaches that facilitate a good forecasting result. The present paper describes assessments based on a combination of different neural network models, tested to forecast steel production in the world. The main goal is to find the best machine learning model that fits the steel production data in the world to make a forecast for a nine-year period. The study is important for understanding the behavior of the models and sensitivity to hyperparameters of convolutional LSTM and GRU recurrent neural networks. The results show that for long-term prediction, the GRU model is easier to train and provides better results. The article contributes to the validation of the use of other variables that are correlated with the steel production variable, thus increasing forecast accuracy. | URI: | https://hdl.handle.net/10316/114907 | ISSN: | 2076-3417 | DOI: | 10.3390/app13010178 | Rights: | openAccess |
Appears in Collections: | I&D CEMMPRE - Artigos em Revistas Internacionais I&D ISR - Artigos em Revistas Internacionais FCTUC Eng.Electrotécnica - Artigos em Revistas Internacionais |
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Forecasting-Steel-Production-in-the-WorldAssessments-Based-on-Shallow-and-Deep-Neural-NetworksApplied-Sciences-Switzerland.pdf | 1.52 MB | Adobe PDF | View/Open |
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