Previsão de demandas em uma rede de postos de combustíveis, com auxílio de séries temporais, métodos causais e redes neurais artificiais

Abstract

The object of this study is demonstrate with three quantity ways about ideas of demand, what is: time series, casual method and artificial neural system. And with this results, organize comparations about routine data and find the better method to be apply in a gas station. Based on an error measurement, where the one presenting the lowest is considered the best method. In order to achieve this, the objective of this study is to evaluate which of the mathematical methods can be more efficient in forecasting demand. In analyses will be use Excel®, Crystal Ball® and Matlab®. After all work, it is possible see artificial neural system with the best results, in second place we have time series and for last casual method. When we consider the week analyses, it's verified the best results, because of data control and outliers finish. In this form, it is correct consider the artificial neural with the best method when the subject is ideas of demand.