An Early Warning Method for Basic Commodities Price Based on Artificial Neural Networks

  • Jesús SilvaEmail author
  • Noel Varela
  • Hugo Martínez Caraballo
  • Jesús García Guiliany
  • Luis Cabas Vásquez
  • Jorge Navarro Beltrán
  • Nadia León Castro
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11554)


The prices of products belonging to the basic family basket are an important component in the income of producers and consumer spending; its excessive variations constitute a source of uncertainty and risk that affects producers, since it prevents the realization of long-term investment plans, and can refuse lenders to grant them credit. His study to identify these variations, as well as to detect their sources, is then of great importance. The analysis of the variations of the prices of the basic products over time, include seasonal patterns, annual fluctuations, trends, cycles and volatility. Because of the advance in technology, applications have been developed based on Artificial Neural Networks (ANN) which have helped the development of massive sales forecast on consumer products, improving the accuracy of traditional forecasting systems. This research uses the RNA to develop an early warning system for facing the increase in basic agricultural products, considering seasonal factors.


Support vector machines Cyclic variation Predictive model Multilayer perceptron Multiple Input Multiple Output Forecast 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jesús Silva
    • 1
    Email author
  • Noel Varela
    • 2
  • Hugo Martínez Caraballo
    • 3
  • Jesús García Guiliany
    • 3
  • Luis Cabas Vásquez
    • 4
  • Jorge Navarro Beltrán
    • 4
  • Nadia León Castro
    • 4
  1. 1.Universidad Peruana de Ciencias AplicadasLimaPeru
  2. 2.Universidad de la CostaBarranquillaColombia
  3. 3.Universidad Simón BolívarBarranquillaColombia
  4. 4.Corporación Universitaria LatinoamericanaBarranquillaColombia

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