01498nas a2200217   4500000000100000000000100001008004100002260001200043653002800055653003100083653001600114100003000130700003400160700003600194245006500230856009500295300001000390490000600400520086000406022001401266       2019                            d  c03/201910aArtificial Intelligence10aArtificial Neural Networks10aForecasting1 aRosa María Cantón Croda1 aDamián Emilio Gibaja Romero1 aSantiago Omar Caballero Morales00aSales Prediction through Neural Networks for a Small Dataset  uhttp://www.ijimai.org/journal/sites/default/files/files/2018/04/ijimai_5_4_4_pdf_17317.pdf  a35-410 v53 aSales forecasting allows firms to plan their production outputs, which contributes to optimizing firms' inventory management via a cost reduction. However, not all firms have the same capacity to store all the necessary information through time. So, time-series with a short length are common within industries, and problems arise due to small time series does not fully capture sales' behavior. In this paper, we show the applicability of neural networks in a case where a company reports a short time-series given the changes in its warehouse structure. Given the neural networks independence form statistical assumptions, we use a multilayer-perceptron to get the sales forecasting of this enterprise. We find that learning rates variations do not significantly increase the computing time, and the validation fails with an error minor to five percent.  a1989-1660