Advertisement

An Early Warning Method for Agricultural Products Price Spike Based on Artificial Neural Networks Prediction

  • Jesús SilvaEmail author
  • Mercedes Gaitán Angulo
  • Jenny Romero Borré
  • Liliana Patricia Lozano Ayarza
  • Omar Bonerge Pineda Lezama
  • Zuleima del Carmen Martínez Galán
  • Jorge Navarro Beltran
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11537)

Abstract

In general, the agricultural producing sector is affected by the diversity in supply, mostly from small companies, in addition to the rigidity of the demand, the territorial dispersion, the seasonality or the generation of employment related to the rural environment. These characteristics differentiate the agricultural sector from other economic sectors. On the other hand, the volatility of prices payed by producers, the high cost of raw materials, and the instability of both domestic and international markets are factors which have eroded the competitiveness and profitability of the agricultural sector. Because of the advance in technology, applications have been developed based on Artificial Neural Networks (ANN) which have helped the development of 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 agricultural products, considering macro and micro economic variables and factors related to the seasons of the year.

Keywords

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

References

  1. 1.
    Fonseca, Z., et al.: Encuesta Nacional de la Situación Nutricional en Colombia 2010. Da Vinci, Bogotá (2011)Google Scholar
  2. 2.
    Instituto Colombiano de Bienestar Familiar (ICBF): Ministerio de Salud y Protección Social, Instituto Nacional de Salud (INS) Departamento Administrativo para la Prosperidad Social, Universidad Nacional de Colombia. The National Survey of the Nutritional Situation of Colombia (ENSIN) (2015)Google Scholar
  3. 3.
    Food and Agriculture Organization of the United Nations (FAO): Pan American Health Organization (PAHO), World Food Programme (WFP), United nations International Children’s Emergency Fund (UNICEF). Panorama of Food and Nutritional Security in Latin America and the Caribbean. Inequality and Food Systems, Santiago (2018)Google Scholar
  4. 4.
    Frank, R.J., Davey, N., Hunt, S.P.: Time series prediction and neural networks. J. Intell. Rob. Syst. 31(3), 91–103 (2001)zbMATHCrossRefGoogle Scholar
  5. 5.
    Haykin, S.: Neural Networks and Learning Machines. Prentice Hall International, New Jersey (2009)Google Scholar
  6. 6.
    Jain, A.K., Mao, J., Mohiuddin, K.M.: Artificial neural networks: a tutorial. IEEE Comput. 29(3), 1–32 (1996)CrossRefGoogle Scholar
  7. 7.
    Kulkarni, S., Haidar, I.: Forecasting model for crude oil price using artificial neural networks and commodity future prices. Int. J. Comput. Sci. Inf. Secur. 2(1), 81–89 (2008)Google Scholar
  8. 8.
    McNelis, P.D.: Neural Networks in Finance: Gaining Predictive Edge in the Market, vol. 59, pp. 1–22. Elsevier Academic Press, Massachusetts (2005)CrossRefGoogle Scholar
  9. 9.
    Mombeini, H., Yazdani-Chamzini, A.: Modelling gold price via artificial neural network. J. Econ. Bus. Manag. 3(7), 699–703 (2015)CrossRefGoogle Scholar
  10. 10.
    Sevim, C., Oztekin, A., Bali, O., Gumus, S., Guresen, E.: Developing an early warning system to predict currency crises. Eur. J. Oper. Res. 237(1), 1095–1104 (2014)CrossRefGoogle Scholar
  11. 11.
    Zhang, G.P.: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50(1), 159–175 (2003)zbMATHCrossRefGoogle Scholar
  12. 12.
    Horton, N.J., Kleinman, K.: Using R For Data Management, Statistical Analysis, and Graphics. CRC Press, Clermont (2010)zbMATHGoogle Scholar
  13. 13.
    Chang, P.C., Wang, Y.W.: Fuzzy Delphi and backpropagation model for sales forecasting in PCB industry. Expert Syst. Appl. 30(4), 715–726 (2006)CrossRefGoogle Scholar
  14. 14.
    Lander, J.P.: R for Everyone: Advanced Analytics and Graphics. Addison-Wesley Professional, Boston (2014)Google Scholar
  15. 15.
    Chopra, S., Meindl, P.: Supply Chain Management: Strategy, Planning and Operation. Prentice Hall, NJ (2001)Google Scholar
  16. 16.
    Izquierdo, N.V., Lezama, O.B.P., Dorta, R.G., Viloria, A., Deras, I., Hernandez-Fernandez, L.: Fuzzy logic applied to the performance evaluation. Honduran coffee sector case. In: Tan, Y., Shi, Y., Tang, Q. (eds.) Advances in Swarm Intelligence. ICSI 2018. LNCS, vol. 10942, p. 164--173. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-93818-9_16Google Scholar
  17. 17.
    Babu, C.N., Reddy, B.E.: A moving-average filter based hybrid ARIMA–ANN model for forecasting time series data. Appl. Soft Comput. 23(1), 27–38 (2014)CrossRefGoogle Scholar
  18. 18.
    Cai, Q., Zhang, D., Wu, B., Leung, S.C.: A novel stock forecasting model based on fuzzy time series and genetic algorithm. Procedia Comput. Sci. 18(1), 1155–1162 (2013)CrossRefGoogle Scholar
  19. 19.
    Egrioglu, E., Aladag, C.H., Yolcu, U.: Fuzzy time series forecasting with a novel hybrid approach combining fuzzy c-means and neural networks. Expert Syst. Appl. 40(1), 854–857 (2013)CrossRefGoogle Scholar
  20. 20.
    Kourentzes, N., Barrow, D.K., Crone, S.F.: Neural network ensemble operators for time series forecasting. Expert Syst. Appl. 41(1), 4235–4244 (2014)CrossRefGoogle Scholar
  21. 21.
    Departamento Administrativo Nacional de Estadística-DANE.: Manual Técnico del Censo General. DANE, Bogotá (2018)Google Scholar
  22. 22.
    Fajardo-Toro, C.H., Mula, J., Poler, R.: Adaptive and hybrid forecasting models—a review. In: Ortiz, Á., Andrés Romano, C., Poler, R., García-Sabater, J.-P. (eds.) Engineering Digital Transformation. LNMIE, pp. 315–322. Springer, Cham (2019).  https://doi.org/10.1007/978-3-319-96005-0_38CrossRefGoogle Scholar
  23. 23.
    Deliana, Y., Rum, I.A.: Understanding consumer loyalty using neural network. Pol. J. Manag. Stud. 16(2), 51–61 (2017)CrossRefGoogle Scholar
  24. 24.
    Scherer, M.: Waste flows management by their prediction in a production company. J. Appl. Math. Comput. Mech. 16(2), 135–144 (2017)CrossRefGoogle Scholar
  25. 25.
    Chang, O., Constante, P., Gordon, A., Singana, M.: A novel deep neural network that uses space-time features for tracking and recognizing a moving object. J. Artif. Intell. Soft Comput. Res. 7(2), 125–136 (2017)CrossRefGoogle Scholar
  26. 26.
    Sekmen, F., Kurkcu, M.: An early warning system for turkey: the forecasting of economic crisis by using the artificial neural networks. Asian Econ. Financ. Rev. 4(1), 529–543 (2014)Google Scholar
  27. 27.
    Ke, Y., Hagiwara, M.: An English neural network that learns texts, finds hidden knowledge, and answers questions. J. Artif. Intell. Soft Comput. Res. 7(4), 229–242 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jesús Silva
    • 1
    Email author
  • Mercedes Gaitán Angulo
    • 2
  • Jenny Romero Borré
    • 3
  • Liliana Patricia Lozano Ayarza
    • 3
  • Omar Bonerge Pineda Lezama
    • 4
  • Zuleima del Carmen Martínez Galán
    • 2
  • Jorge Navarro Beltran
    • 5
  1. 1.Universidad Peruana de Ciencias AplicadasLimaPeru
  2. 2.Corporación Universitaria Empresarial de Salamanca (CUES)BarranquillaColombia
  3. 3.Universidad de la CostaBarranquilla, AtlánticoColombia
  4. 4.Universidad Tecnológica Centroamericana (UNITEC)San Pedro SulaHonduras
  5. 5.Corporación Universitaria LatinoamericanaBarranquillaColombia

Personalised recommendations