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Deep Learning Model for Forecasting Financial Sales Based on Long Short-Term Memory Networks

  • Pablo F. Ordoñez-OrdoñezEmail author
  • Martha C. Suntaxi Sarango
  • Cristian Narváez
  • Maria del Cisne Ruilova Sánchez
  • Mario Enrique Cueva-Hurtado
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1066)

Abstract

The present article presents a model LSTM for the forecast of product sales, an alternative in deep learning for this type of dilemmas and not frequent in the area of financial knowledge. It was approached as a time series and following the steps for the construction of models with machine learning. The ILE company of Ecuador provided the data, between 2011 and 2018. The results showed this model has a minimum RMSE error of 2.20 compared to another two models: ARIMA and Single Perceptron.

Keywords

LSTM Deep learning Machine learning Sales forecast 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Facultad de Energí­aUniversidad Nacional de LojaLojaEcuador
  2. 2.Escuela Técnica Superior de Ingenierí­­a de Sistemas InformáticosUniversidad Politécnica de MadridMadridSpain

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