Bayesian-optimized ensemble deep learning models for demand forecasting in the volatile situations: A case study of grocery demand during Covid-19 outbreaks

Bayesian-optimized ensemble deep learning models for demand forecasting in the volatile situations: A case study of grocery demand during Covid-19 outbreaks

Purpose: Lockdown and movement restrictions that imposed by governments have significantly changed customers behavior, making the planning and decision-making processes more challenging. Providing accurate estimation of the demand, enable managers to take more successful decisions and allow optimizi...

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Bibliographic Details
Main Authors: Nader AL Theeb, Hazem Smadi, Naser Al-qaydeh
Format: Article
Language:English
Published: OmniaScience 2025-03-01
Series:Journal of Industrial Engineering and Management
Subjects:
demand prediction, machine learning, ensemble model, bayesian optimization, long short-term memory (lstm), gated recurrent unit (gru)
Online Access:http://www.jiem.org/index.php/jiem/article/view/6571
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http://www.jiem.org/index.php/jiem/article/view/6571

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