Sales Prediction through Neural Networks for a Small Dataset

Authors

Rosa María Cantón Croda
Damián Emilio Gibaja Romero
Santiago Omar Caballero Morales

DOI

10.9781/ijimai.2018.04.003

Abstract

Sales 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.

Source Publication

International Journal of Interactive Multimedia and Artificial Intelligence

Recommended Citation

Cantón Croda, Rosa María; Gibaja Romero, Damián Emilio; and Caballero Morales, Santiago Omar (2019) "Sales Prediction through Neural Networks for a Small Dataset," International Journal of Interactive Multimedia and Artificial Intelligence: Vol. 5: Iss. 4, Article 9.
DOI: 10.9781/ijimai.2018.04.003
Available at: https://ijimai.researchcommons.org/ijimai/vol5/iss4/9