An Approach for Demand Forecasting in Steel Industries Using Ensemble Learning
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...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2022/9928836 |
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| author | S. M. Taslim Uddin Raju Amlan Sarker Apurba Das Md. Milon Islam Mabrook S. Al-Rakhami Atif M. Al-Amri Tasniah Mohiuddin Fahad R. Albogamy |
| author_facet | S. M. Taslim Uddin Raju Amlan Sarker Apurba Das Md. Milon Islam Mabrook S. Al-Rakhami Atif M. Al-Amri Tasniah Mohiuddin Fahad R. Albogamy |
| author_sort | S. M. Taslim Uddin Raju |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-23a5356c1d7046c38c36a0e84b886421 |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-23a5356c1d7046c38c36a0e84b8864212025-02-03T06:14:09ZengWileyComplexity1099-05262022-01-01202210.1155/2022/9928836An Approach for Demand Forecasting in Steel Industries Using Ensemble LearningS. M. Taslim Uddin Raju0Amlan Sarker1Apurba Das2Md. Milon Islam3Mabrook S. Al-Rakhami4Atif M. Al-Amri5Tasniah Mohiuddin6Fahad R. Albogamy7Department of Computer Science and EngineeringDepartment of Computer Science and EngineeringDepartment of Industrial Engineering and ManagementDepartment of Computer Science and EngineeringResearch Chair of Pervasive and Mobile ComputingResearch Chair of Pervasive and Mobile ComputingDepartment of Computer Science and EngineeringComputer Sciences ProgramThis 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.http://dx.doi.org/10.1155/2022/9928836 |
| spellingShingle | S. M. Taslim Uddin Raju Amlan Sarker Apurba Das Md. Milon Islam Mabrook S. Al-Rakhami Atif M. Al-Amri Tasniah Mohiuddin Fahad R. Albogamy An Approach for Demand Forecasting in Steel Industries Using Ensemble Learning Complexity |
| title | An Approach for Demand Forecasting in Steel Industries Using Ensemble Learning |
| title_full | An Approach for Demand Forecasting in Steel Industries Using Ensemble Learning |
| title_fullStr | An Approach for Demand Forecasting in Steel Industries Using Ensemble Learning |
| title_full_unstemmed | An Approach for Demand Forecasting in Steel Industries Using Ensemble Learning |
| title_short | An Approach for Demand Forecasting in Steel Industries Using Ensemble Learning |
| title_sort | approach for demand forecasting in steel industries using ensemble learning |
| url | http://dx.doi.org/10.1155/2022/9928836 |
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