Forecasting Steel Production in the World—Assessments Based on Shallow and Deep Neural Networks

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...

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Bibliographic Details
Published in:Applied sciences 2023-01, Vol.13 (1), p.178
Main Authors: Mateus, Balduíno César, Mendes, Mateus, Farinha, José Torres, Cardoso, António J. Marques, Assis, Rui, da Costa, Lucélio M.
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Assessments
Automobile industry
CNN
Computer simulation
Consumption
Decision making
Forecasting
LSTM
Machine learning
MLP
Neural networks
Recurrent neural networks
Steel
Steel industry
Steel production
Time series
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app13010178