Applied Intelligence

, Volume 34, Issue 3, pp 331–346 | Cite as

A study on the medium-term forecasting using exogenous variable selection of the extra-virgin olive oil with soft computing methods

  • Antonio J. RiveraEmail author
  • Pedro Pérez-Recuerda
  • María Dolores Pérez-Godoy
  • María Jose del Jesús
  • María Pilar Frías
  • Manuel Parras
Original Paper


Time series forecasting is an important task for the business sector. Agents involved in the olive oil sector consider that, for the olive oil price, medium-term predictions are more important than short-term predictions. In collaboration with these agents the forecasting of the price of extra-virgin olive oil six months ahead has been established as the aim of this work. According to expert opinion, the use of exogenous variables and technical indicators can help in this task and must be included in the forecasting process. The amount of variables that can be considered makes necessary the use of feature selection algorithms in order to reduce the number of variables and to increase the interpretability and usefulness of the obtained forecasting system. Thus, in this paper CO2RBFN, a cooperative-competitive algorithm for Radial Basis Function Network design, and other soft computing methods have been applied to the data sets with the whole set of input variables and to the data sets with the selected set of input variables. The experimentation carried out shows that CO2RBFN obtains the best results in medium term forecasting for olive oil prices with the whole and with the selected set of input variables. Moreover, the feature selection methods applied to the data sets highlighted some influential variables which could be considered not only for the prediction but also for the description of the complex process involved in the medium-term forecasting of the olive oil price.


Forecasting Olive-oil price Feature selection Technical indicators Cooperative-competitive Evolutionary algorithms 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Achelis S (2000) Technical analysis from A to Z, 2nd edn. McGraw-Hill, New York Google Scholar
  2. 2.
    Alcalá-Fdez J, Fernández A, García S, Del Jesus M, Ventura S, Garrell J, Otero J, Romero C, Bacardit J, Herrera F (2009) Keel: a software tool to assess evolutionary algorithms for data mining problems. Soft Comput 13(3):307–318 CrossRefGoogle Scholar
  3. 3.
    Alcalá-Fdez J, Luengo J, Derrac J, García S, Sánchez L, Herrera F (2011) Keel data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J Multiple-Valued Logic Soft Comput 17(2–3):255–287 Google Scholar
  4. 4.
    Atsalakis G, Valavanis K (2009) Surveying stock market forecasting techniques—part ii: Soft computing methods. Expert Syst Appl 36(3, Part 2):5932–5941 CrossRefGoogle Scholar
  5. 5.
    Azadeh A, Saberi M, Ghaderi S, Gitiforouz A, Ebrahimipour V (2008) Improved estimation of electricity demand function by integration of fuzzy system and data mining approach. Energy Convers Manag 49(8):2165–2177 CrossRefGoogle Scholar
  6. 6.
    Aznarte J, Benítez J, Lugilde D, de Linares C, de la Guardia C, Sánchez F (2007) Forecasting airborne pollen concentration time series with neural and neuro-fuzzy models. Expert Syst Appl 32(4):1218–1225 CrossRefGoogle Scholar
  7. 7.
    Bäck T, Hammel U, Schwefel H (1997) Evolutionary computation: comments on the history and current state. IEEE Trans Evol Comput 1(1):3–17 CrossRefGoogle Scholar
  8. 8.
    Bao D (2008) A generalized model for financial time series representation and prediction. Appl Intell 29(1):1–11 CrossRefGoogle Scholar
  9. 9.
    Box G, Jenkins G, Reinsel G (2008) Time series analysis: forecasting and control, 4th edn. Wiley, New York zbMATHGoogle Scholar
  10. 10.
    Broomhead D, Lowe D (1988) Multivariable functional interpolation and adaptive networks. Complex Syst 2:321–355 MathSciNetzbMATHGoogle Scholar
  11. 11.
    Buchtala O, Klimek M, Sick B (2005) Evolutionary optimization of radial basis function classifiers for data mining applications. IEEE Trans Syst Man Cybern, Part B, Cybern 35(5):928–947 CrossRefGoogle Scholar
  12. 12.
    Chen C, Wu Y, Luk B (1999) Combined genetic algorithm optimization and regularized orthogonal least squares learning for radial basis function networks. IEEE Trans Neural Netw 10(5):1239–1243 CrossRefGoogle Scholar
  13. 13.
    Chizi B, Maimon O (2005) Dimension reduction and feature selection. In: The data mining and knowledge discovery handbook, pp 93–111. Springer, Berlin CrossRefGoogle Scholar
  14. 14.
    Co H, Boosarawongse R (2007) Forecasting Thailand’s rice export: statistical techniques vs. artificial neural networks. Comput Ind Eng 53(4):610–627 CrossRefGoogle Scholar
  15. 15.
    Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30 MathSciNetGoogle Scholar
  16. 16.
    Dourra H, Siy P (2002) Investment using technical analysis and fuzzy logic. Fuzzy Sets Syst 127(2):221–240 MathSciNetCrossRefGoogle Scholar
  17. 17.
    Du H, Zhang N (2008) Time series prediction using evolving radial basis function networks with new encoding scheme. Neurocomputing 71:1388–1400 CrossRefGoogle Scholar
  18. 18.
    Duda R, Hart P (1973) Pattern classification and scene analysis. Wiley, New York zbMATHGoogle Scholar
  19. 19.
    Elder J, Pregibon D (1996) A statistical perspective on knowledge discovery in databases. In: Advances in knowledge discovery and data mining. AAAI Press/MIT Press, Menlo Park/Cambridge Google Scholar
  20. 20.
    Fan R, Chen P, Lin C (2005) Working set selection using the second order information for training svm. J Mach Learn Res 6:1889–1918 MathSciNetGoogle Scholar
  21. 21.
    Fayyad U, Irani K (1993) Multi-interval discretisation of continuous valued attributes for classification learning. In: Proceedings of the thirteenth international joint conference on artificial intelligence. Morgan Kaufmann, San Mateo Google Scholar
  22. 22.
    García F, García M, Melián B, Moreno J, Marcos J (2006) Solving feature subset selection problem by a parallel scatter search. Eur J Oper Res 169:477–489 CrossRefzbMATHGoogle Scholar
  23. 23.
    García R, Benítez J, Saíz G (2008) Feature selection for time series forecasting: a case study. In: International conference on hybrid intelligent systems, pp 555–560 Google Scholar
  24. 24.
    García S, Fernández A, Luengo J, Herrera F (2009) A study of statistical techniques and performance measures for genetics–based machine learning: accuracy and interpretability. Soft Comput 13(10):959–977 CrossRefGoogle Scholar
  25. 25.
    Garcíia S, Fernández A, Luengo J, Herrera F (2010) Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power. Inf Sci 180:2044–2064 CrossRefGoogle Scholar
  26. 26.
    García S, Herrera F (2008) An extension on “statistical comparisons of classifiers over multiple data sets” for all pairwise comparisons. J Mach Learn Res 9:2677–2694 Google Scholar
  27. 27.
    García S, Molina D, Lozano M, Herrera F (2009) A study on the use of non-parametric test for analyzing the evolutionary algorithms’ behaviour: a case study on the cec’2005 special session on real parameter optimization. J Heuristics 15(6):617–644 CrossRefzbMATHGoogle Scholar
  28. 28.
    Ghost J, Deuser L, Beck S (1992) A neural network based hybrid system for detection characterization and classification of short-duration oceanic signals. IEEE J Ocean Eng 17(4):351–363 CrossRefGoogle Scholar
  29. 29.
    Goldberg D (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading zbMATHGoogle Scholar
  30. 30.
    Goldberg D, Richardson J (1987) Genetic algorithms with sharing for multimodal function optimization. In: Proceedings of the second international conference on genetic algorithms, pp 41–49 Google Scholar
  31. 31.
    Golub G, Van Loan C (1996) Matrix computations, 3rd edn. Johns Hopkins University Press, Baltimore zbMATHGoogle Scholar
  32. 32.
    Guillén A, Rubio G, Toda I, Rivera A, Pomares H, Rojas I (2010) Applying multiobjective rbfnns optimization and feature selection to a mineral reduction problem. Expert Syst Appl 37(6):4050–4057 CrossRefGoogle Scholar
  33. 33.
    Gütlein M, Frank E, Hall M, Karwath A (1999) Large scale attribute selection using wrappers. In: Proceedings of the IEEE symposium on computational intelligence and data mining Google Scholar
  34. 34.
    Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182 CrossRefzbMATHGoogle Scholar
  35. 35.
    Gwo-Fong L, Lu-Hsien C (2005) Time series forecasting by combining the radial basis function network and the self-organizing map. Hydrol Process 19:1925–1937 CrossRefGoogle Scholar
  36. 36.
    Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten I (2009) The Weka data mining software: an update. SIGKDD Explorations 11(1) Google Scholar
  37. 37.
    Hall M, Smith L (1999) Feature selection for machine learning: comparing a correlation-based filter approach to the wrapper. In: Twelfth international FLAIRS conference. AAAI Press/MIT Press, Menlo Park/Cambridge Google Scholar
  38. 38.
    Harpham C, Dawson C, Brown M (2004) A review of genetic algorithms applied to training radial basis function networks. Neural Comput Appl 13:193–201 CrossRefGoogle Scholar
  39. 39.
    Haykin S (1998) Neural networks: a comprehensive foundation, 2nd edn. Prentice Hall, New York Google Scholar
  40. 40.
    Holland J (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor Google Scholar
  41. 41.
    Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6:65–70 MathSciNetzbMATHGoogle Scholar
  42. 42.
    Howard L, D’Angelo D (1995) The ga-p: a genetic algorithm and genetic programming hybrid. IEEE Intell Syst 10(3):11–15 Google Scholar
  43. 43.
    Hsu W, Lee ML, Wang J (2007) Temporal and spatio-temporal data mining. IGI Publishing, Hershey Google Scholar
  44. 44.
    Jang J (1993) Anfis: adaptative-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685 MathSciNetCrossRefGoogle Scholar
  45. 45.
    Khashei M, Reza Hejazi S, Bijari M (2008) A new hybrid artificial neural networks and fuzzy regression model for time series forecasting. Fuzzy Sets Syst 159(7):769–786 CrossRefzbMATHGoogle Scholar
  46. 46.
    Lee C, Chiang Y, Shih C, Tsai C (2009) Noisy time series prediction using m-estimator based robust radial basis function neural networks with growing and pruning techniques. Expert Syst Appl 36(3):4717–4724 CrossRefGoogle Scholar
  47. 47.
    Lendasse A, de Bodt E, Wertz V, Verleysen M (2000) Non-linear financial time series forecasting—application to the bel 20 stock market index. Eur J Econ Soc Syst 14(1):81–91 CrossRefzbMATHGoogle Scholar
  48. 48.
    Liang G, Xu W, He Y, Zhao Y (2008) Study and application of pso-rbfnn model to nonlinear time series forecasting for geotechnical engineering. Yantu Lixue (Rock Soil Mech) 29(4):995–1000 Google Scholar
  49. 49.
    Lin Z, Zhang D, Gao L, Kong Z (2008) Using an adaptive self-tuning approach to forecast power loads. Neurocomputing 71(4–6):559–563 CrossRefGoogle Scholar
  50. 50.
    Liu J, Kwong R (2007) Automatic extraction and identification of chart patterns towards financial forecast. Appl Soft Comput 4:1197–1208 CrossRefGoogle Scholar
  51. 51.
    Maimon OLR (2005) The data mining and knowledge discovery handbook. Springer, Berlin CrossRefGoogle Scholar
  52. 52.
    Maimon O, Last M (2000) Knowledge discovery and data mining—the info-fuzzy network (ifn) methodology. Kluwer Academic, Dordrecht zbMATHGoogle Scholar
  53. 53.
    Mandani E, Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man-Mach Stud 7(1):1–13 CrossRefGoogle Scholar
  54. 54.
    Meng K, Dong Z, Wong K (2009) Self-adaptive radial basis function neural network for short-term electricity price forecasting. IET Gener Transm Distrib 4:325–335 CrossRefGoogle Scholar
  55. 55.
    Mochón A, Quintana D, Sáez Y, Isasi P (2008) Soft computing techniques applied to finance. Appl Intell 29(2):111–115 CrossRefGoogle Scholar
  56. 56.
    Moller F (1990) A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw 6:525–533 CrossRefGoogle Scholar
  57. 57.
    Moshiri S, Cameron N, Scuse D (1999) Static dynamic, and hybrid neural networks in forecasting inflation. Comput Econ 14(3):219–235 CrossRefzbMATHGoogle Scholar
  58. 58.
    Murphy J (1999) Technical analysis of the financial markets: a comprehensive guide to trading methods and applications. New York Institute of Finance, New York Google Scholar
  59. 59.
    Nekoukar V, Beheshti MH (2009) A local linear radial basis function neural network for financial time-series forecasting. Appl Intell 33(3):352–356 CrossRefGoogle Scholar
  60. 60.
    Park J, Sandberg I (1993) Universal approximation and radial basis function network. Neural Comput 5(2):305–316 CrossRefGoogle Scholar
  61. 61.
    Pedrycz W (1998) Conditional fuzzy clustering in the design of radial basis function neural networks. IEEE Trans Neural Netw 9(4):601–612 CrossRefGoogle Scholar
  62. 62.
    Pérez-Godoy M, Pérez P, Rivera A, del Jesus M, Carmona C, Frías M, Parras M (2010) CO2RBFN for short-term forecasting of the extra virgin olive oil price in the Spanish market. Int J Hybrid Intell Syst 7(1):75–87 Google Scholar
  63. 63.
    Pérez-Godoy M, Rivera A, del Jesus M, Berlanga F (2010) CO2RBFN: an evolutionary cooperative-competitive RBFN design algorithm for classification problems. Soft Comput 14(9):953–971 CrossRefGoogle Scholar
  64. 64.
    Pino R, Parreno J, Gomez A, Priore P (2008) Forecasting next-day price of electricity in the Spanish energy market using artificial neural networks. Eng Appl Artif Intell 21(1):53–62 CrossRefGoogle Scholar
  65. 65.
    Potter M, De Jong K (2000) Cooperative coevolution: an architecture for evolving coadapted subcomponents. Evol Comput 8(1):1–29 CrossRefGoogle Scholar
  66. 66.
    Press W, Flannery B, Teukolski S, Vetterling W (1988) Numerical recipes in C. Cambridge University Press, Cambridge zbMATHGoogle Scholar
  67. 67.
    Rivas V, Merelo J, Castillo P, Arenas M, Castellano J (2004) Evolving rbf neural networks for time-series forecasting with evrbf. Inf Sci 165:207–220 MathSciNetCrossRefGoogle Scholar
  68. 68.
    Rivera A, Rojas I, Ortega J, del Jesus M (2007) A new hybrid methodology for cooperative-coevolutionary optimization of radial basis function networks. Soft Comput 11(7):655–668 CrossRefGoogle Scholar
  69. 69.
    Roddick J, Spiliopoulou M (1999) A bibliography of temporal spatial and spatio-temporal data mining research. ACM SIGKDD Explor Newsl 1(1):34–38 CrossRefGoogle Scholar
  70. 70.
    Sánchez L, Couso I (2000) Fuzzy random variables-based modeling with ga-p algorithms. In: Bouchon B, Yager RR, Zadeh L (Eds.) Information, uncertainty and fusion, pp 245–256 Google Scholar
  71. 71.
    Sheskin D (2006) Handbook of parametric and nonparametric statistical procedures, 2nd edn. Chapman & Hall/CRC Press, London/Boca Raton Google Scholar
  72. 72.
    Sheta A, De Jong K (2001) Time-series forecasting using ga-tuned radial basis functions. Inf Sci 133:221–228 CrossRefzbMATHGoogle Scholar
  73. 73.
    Tsay RS (2010) Analysis of financial time series, 3rd edn (2010) Google Scholar
  74. 74.
    Ture M, Kurt I (2006) Comparison of four different time series methods to forecast hepatitis A virus infection. Expert Syst Appl 31(1):41–46 CrossRefGoogle Scholar
  75. 75.
    Vanstone B, Finnie G (2009) An empirical methodology for developing stockmarket trading systems using artificial neural networks. Expert Syst Appl 36(3, Part 2), 6668–6680 CrossRefGoogle Scholar
  76. 76.
    Vanstone B, Hahn T (2010) Designing stock market trading systems: with and without soft computing. Harriman House, Petersfield Google Scholar
  77. 77.
    Versace M, Bhatt R, Hinds O, Shiffer M (2004) Predicting the exchange traded fund dia with a combination of genetic algorithms and neural networks. Expert Syst Appl 27(3):417–425 CrossRefGoogle Scholar
  78. 78.
    Whitehead B, Choate T (1996) Cooperative-competitive genetic evolution of radial basis function centers and widths for time series prediction. IEEE Trans Neural Netw 7(4):869–880 CrossRefGoogle Scholar
  79. 79.
    Widrow B, Lehr M (1990) 30 years of adaptive neural networks: perceptron, madaline and backpropagation. Proc IEEE 78(9):1415–1442 CrossRefGoogle Scholar
  80. 80.
    Wilcoxon F (1945) Individual comparisons by ranking methods. Biometrics 1:80–83 CrossRefGoogle Scholar
  81. 81.
    Xiong Q, Yong S, Shi W, Chen J, Liang Y (2005) The research of forecasting model based on rbf neural network. In: Proceedings of the 2005 international conference on neural networks and brain, pp 1032–1035 CrossRefGoogle Scholar
  82. 82.
    Yun Z, Quan Z, Caixin S, Shaolan L, Yuming L, Yang S (2008) Rbf neural network and anfis-based short-term load forecasting approach in real-time price environment. IEEE Trans Power Syst 23(3):853–858 CrossRefGoogle Scholar
  83. 83.
    Zar J (1999) Biostatistical analysis. Prentice Hall, Upper Saddle River Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Antonio J. Rivera
    • 1
    Email author
  • Pedro Pérez-Recuerda
    • 1
  • María Dolores Pérez-Godoy
    • 1
  • María Jose del Jesús
    • 1
  • María Pilar Frías
    • 2
  • Manuel Parras
    • 3
  1. 1.Dept. of Computer ScienceUniversity of JaénJaénSpain
  2. 2.Dept. of Statistics and Operation ResearchUniversity of JaénJaénSpain
  3. 3.Dept. of MarketingUniversity of JaénJaénSpain

Personalised recommendations