African Journal of
Agricultural Research

  • Abbreviation: Afr. J. Agric. Res.
  • Language: English
  • ISSN: 1991-637X
  • DOI: 10.5897/AJAR
  • Start Year: 2006
  • Published Articles: 6932

Full Length Research Paper

Forecast of prices for horticultural products with the use of artificial neural networks

Celso Correia de Souza
  • Celso Correia de Souza
  • Agro-industrial Production and Management at Universidade Anhanguera Uniderp ? Campo Grande, MS, Brazil.
  • Google Scholar
Jose Francisco dos Reis Neto
  • Jose Francisco dos Reis Neto
  • Agro-industrial Production and Management at Universidade Anhanguera Uniderp ? Campo Grande, MS, Brazil.
  • Google Scholar
Daniel Massen Frainer
  • Daniel Massen Frainer
  • Agro-industrial Production and Management at Universidade Anhanguera Uniderp ? Campo Grande, MS, Brazil.
  • Google Scholar
Francisco de Assis Rolim Pereira
  • Francisco de Assis Rolim Pereira
  • Agro-industrial Production and Management at Universidade Anhanguera Uniderp ? Campo Grande, MS, Brazil.
  • Google Scholar
Rafael Gabriel
  • Rafael Gabriel
  • Master in Agro-industrial Production and Management at Universidade Anhanguera Uniderp. Brazil.
  • Google Scholar


  •  Received: 08 April 2015
  •  Accepted: 03 June 2015
  •  Published: 23 July 2015

Abstract

Artificial neural networks (ANN) are becoming increasingly popular, acting as a very important tool to aid in the interpretation of the market. They have been used with benefits in time series analysis, as they provide an easy mathematical treatment and faster results, facilitating decision-making. Currently in the field of business, many systems using neural networks have worked well in identifying complex patterns, learning by experience, reaching conclusions and making predictions. This article deals with the application of ANN for predicting vegetable prices due to seasonality. The networks were trained using time series data for vegetables prices from the database of the Núcleo de Estudos e Pesquisas Econômicas e Sociais (NEPES) {Center for Studies and Economic and Social Research} at Universidade Anhanguera Uniderp of Campo Grande (MS), Brazil. The results were very promising and encouraging because it was possible to forecast prices of these foods over time, serving as a good tool to help entrepreneurs in the horticultural industry. This method is very useful because it can be applied also in the retail trade and industry in helping entrepreneurs in these sectors in decision-making.

 

Key words: Vegetable, time series forecasting, artificial neural networks (ANN) training, artificial neuron, horticultural industry.