Demand Forecasting for an Automotive Company with Neural Network and Ensemble Classifiers Approaches - Artificial Intelligence for Sustainable and Resilient Production Systems PART IV, IFIP WG 5.7 International Conference, APMS 2021 Access content directly
Conference Papers Year : 2021

Demand Forecasting for an Automotive Company with Neural Network and Ensemble Classifiers Approaches

Eleonora Bottani
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Monica Mordonini
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Beatrice Franchi
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  • PersonId : 1237550
Mattia Pellegrino
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Abstract

This work proposes the development and testing of three machine learning technique for demand forecasting in the automotive industry: Artificial Neural Network (ANN) and two types of Ensemble Learning models, i.e. AdaBoost and Gradient Boost. These models demonstrate the great potential that machine learning has over traditional demand forecasting methods. These three models will be compared to each other on the basis of the coefficient of determination R2 and it will be shown which model has the greatest accuracy.
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hal-04030416 , version 1 (16-03-2023)

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Eleonora Bottani, Monica Mordonini, Beatrice Franchi, Mattia Pellegrino. Demand Forecasting for an Automotive Company with Neural Network and Ensemble Classifiers Approaches. IFIP International Conference on Advances in Production Management Systems (APMS), Sep 2021, Nantes, France. pp.134-142, ⟨10.1007/978-3-030-85874-2_14⟩. ⟨hal-04030416⟩
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