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.
Demand Forecasting for an Automotive Company with Neural Network and Ensemble Classifiers Approaches / Bottani, Eleonora; Mordonini, Monica; Franchi, Beatrice; Pellegrino, Mattia. - 630:(2021), pp. 134-142. (Intervento presentato al convegno IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2021 tenutosi a Nantes, France (virtual conference) nel 5-7 settembre 2021) [10.1007/978-3-030-85874-2_14].
Demand Forecasting for an Automotive Company with Neural Network and Ensemble Classifiers Approaches
Bottani Eleonora
;Mordonini Monica;Franchi Beatrice;Pellegrino Mattia
2021-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
