An Approach for Demand Forecasting in Steel Industries Using Ensemble Learning

• Published in: Complexity (New York, N.Y.) Vol. 2022; no. 1
• Main Authors: Raju, S. M. Taslim Uddin, Sarker, Amlan, Das, Apurba, Islam, Md. Milon, Al-Rakhami, Mabrook S., Al-Amri, Atif M., Mohiuddin, Tasniah, Albogamy, Fahad R.
• Format: Journal Article
• Language: English
• Published: Hoboken Hindawi 2022; Hindawi Limited; Hindawi-Wiley
• Subjects: Accuracy; Agribusiness; Algorithms; Artificial intelligence; Artificial neural networks; Crude oil prices; Datasets; Decision making; Decision trees; Demand; Economic forecasting; Ensemble learning; Feature selection; Investigations; Iron and steel industry; Machine learning; Market positioning; Mathematical models; Multilayer perceptrons; Neural networks; Performance enhancement; Pipelines; Regression; Root-mean-square errors; Sales forecasting; Standardization; Steel industry; Steel production; Supply chains; Support vector machines; Time series; Bangladesh
• ISSN: 1076-2787; 1099-0526
• DOI: 10.1155/2022/9928836

This paper aims to introduce a robust framework for forecasting demand, including data preprocessing, data transformation and standardization, feature selection, cross-validation, and regression ensemble framework. Bagging (random forest regression (RFR)), boosting (gradient boosting regression (GBR) and extreme gradient boosting regression (XGBR)), and stacking (STACK) are employed as ensemble models. Different machine learning (ML) approaches, including support vector regression (SVR), extreme learning machine (ELM), and multilayer perceptron neural network (MLP), are adopted as reference models. In order to maximize the determination coefficient (R2) value and reduce the root mean square error (RMSE), hyperparameters are set using the grid search method. Using a steel industry dataset, all tests are carried out under identical experimental conditions. In this context, STACK1 (ELM + GBR + XGBR-SVR) and STACK2 (ELM + GBR + XGBR-LASSO) models provided better performance than other models. The highest accuracies of R2 of 0.97 and 0.97 are obtained using STACK1 and STACK2, respectively. Moreover, the rank according to performances is STACK1, STACK2, XGBR, GBR, RFR, MLP, ELM, and SVR. As it improves the performance of models and reduces the risk of decision-making, the ensemble method can be used to forecast the demand in a steel industry one month ahead.