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

被引：2

作者：

Bottani, Eleonora ^{[1
]}

Mordonini, Monica ^{[1
]}

Franchi, Beatrice ^{[1
]}

Pellegrino, Mattia ^{[1
]}

机构：

[1] Univ Parma, Dept Engn & Architecture, Viale Sci 181-A, I-43124 Parma, Italy

来源：

ADVANCES IN PRODUCTION MANAGEMENT SYSTEMS: ARTIFICIAL INTELLIGENCE FOR SUSTAINABLE AND RESILIENT PRODUCTION SYSTEMS, APMS 2021, PT I | 2021年 / 630卷

关键词：

Demand forecasting; Automotive; ANN; AdaBoost; Gradient Boost;

D O I：

10.1007/978-3-030-85874-2_14

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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 R-2 and it will be shown which model has the greatest accuracy.

引用

页码：134 / 142

页数：9

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