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Demand prediction techniques are used in numerous industry sectors with the aim of adding value to the business of the companies, especially through the search for optimal sizing of production resources. The prediction of demand in restaurants with the intention of balancing the quantity food produced looking for better use of ingredients is a challenge, since factors like the quantity of users, the time of service and the kind of food can be quite variable in this type of problem. The study of queue, in this context, is of paramount importance, given that, knowing its characteristics, it is possible to estimate, by means of prediction, information that can improve service quality. Present work had the purpose of using models based on Artificial Neural Networks (ANN) to perform regressions in a personalized time series, generated through its own methodology with data collected in the restaurant of the IFMG Campus Bambuí. The main objective was to develop a computational model that would be able to describe the behavior for the time intervals in the restaurant customer service. Through this resource, it was possible to generate important information for decision making, such as the peak times of higher and lower demands.

Avaliação de Uma Rede Neural Artificial Como Modelo Regressor Para Séries Temporais