A Revenue Forecast Comparison for the Automobile Industry Using Neural and Linear Regression
DOI:
https://doi.org/10.29327/2384439.3.3-4Keywords:
Automobile industry, revenue, neural networks, linear regressionAbstract
Artificial neural networks (ANNs) are tools used in the construction of complex system models. Their main characteristics include: learning and the reduction of the volume of data for the modelling. This exploratory study compared the performance of the models based on multiple linear regression and the neural networks to forecast the revenues of the automobilistic industry. We used secondary data, regarding the period from 1980 to 2001, collected from ANFAVEA (2002). The results showed that the average error of the forecast model based on neural networks was smaller than the model based on multiple linear regression.
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Copyright (c) 2025 Roberto Giro Moori, André Ng, Roberto Ramos de Morais, Plácido de Jesus da Silva Leitão Junior
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