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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)

Abstract

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.

Keywords

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

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)zbMATHGoogle Scholar
  5. 5.
    Haykin, S.: Neural Networks and Learning Machines. Prentice Hall International, Upper Saddle River (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)Google 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, no. 1, pp. 1–22. Elsevier Academic Press, Massachusetts (2005)Google Scholar
  9. 9.
    Mombeini, H., Yazdani-Chamzini, A.: Modelling gold price via artificial neural network. J. Econ. Bus. Manag. 3(7), 699–703 (2015)Google 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)Google Scholar
  11. 11.
    Zhang, G.P.: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50(1), 159–175 (2003)zbMATHGoogle 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)Google 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, Upper Saddle River (2001)Google Scholar
  16. 16.
    Izquierdo, N.V., Lezama, O.B.P., Dorta, R.G., Viloria, A., Deras, I., Hernández-Fernández, L.: Fuzzy logic applied to the performance evaluation. Honduran coffee sector case. In: Tan, Y., Shi, Y., Tang, Q. (eds.) ICSI 2018. LNCS, vol. 10942, pp. 164–173. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-93818-9_16 Google 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)Google 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)Google 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)Google 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)Google 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_38Google Scholar
  23. 23.
    Deliana, Y., Rum, I.A.: Understanding consumer loyalty using neural network. Pol. J. Manag. Stud. 16(2), 51–61 (2017)Google Scholar
  24. 24.
    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)Google Scholar
  25. 25.
    Scherer, M.: Waste flows management by their prediction in a production company. J. Appl. Math. Comput. Mech. 16(2), 135–144 (2017)Google 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)Google Scholar

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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