An Approach for Demand Forecasting in Steel Industries Using Ensemble Learning

S M Taslim Uddin Raju,Amlan Sarker,Apurba Das,Tasniah Mohiuddin,Md Milon Islam,Fahad R Albogamy,Mabrook S Al-Rakhami,Atif M Al-Amri,Shahzad Sarfraz

doi:10.1155/2022/9928836

Abstract

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 ( R 2 ) 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.

Highlights

Demand forecasting indicates the prediction of the future needs of a product or service [1]
Precise demand forecasting significantly influences improving the performance and durability of the steel industry. is study compares the predictive performance of STACK, GBR, XGBR, and reference models. e ensemble bagging (RFR) regression ensembles and the multilayer perceptron neural network (MLP), extreme learning machine (ELM), and support vector regression (SVR) reference models
While principal component analysis (PCA) and imperialist competitive algorithms (ICA) solely focus on interfeature redundancy, correlationbased feature selection might improve interfeature correlation

Summary

Introduction

Demand forecasting indicates the prediction of the future needs of a product or service [1]. It can lead to overproduction or underproduction All these cases erode the entire supply chain and total income, resulting in opportunity cost. The entire industry setup depends on this demand, such as the amount of raw material, labor, and space. For these whole arrangements, time is a crucial issue, as some processes have predefined deadlines that must be perfectly synchronized. Ey follow the time series of their sales data and often skip factors, such as raw material supply, availability, and the number of workers at the factories, significantly influencing steel production The most important thing is to forecast the demand precisely but the industries do not have any intelligent method to measure the need perfectly. ey follow the time series of their sales data and often skip factors, such as raw material supply, availability, and the number of workers at the factories, significantly influencing steel production

Objectives

Methods

Conclusion