An Automated Hybrid CBR System for Forecasting

  • Florentino Fdez-Riverola
  • Juan M. Corchado
  • Jesús M. Torres
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2416)


A hybrid neuro-symbolic problem solving model is presented in which the aim is to forecast parameters of a complex and dynamic environment in an unsupervised way. In situations in which the rules that determine a system are unknown, the prediction of the parameter values that determine the characteristic behaviour of the system can be a problematic task. The proposed system employs a case-based reasoning model that incorporates a growing cell structures network, a radial basis function network and a set of Sugeno fuzzy models to provide an accurate prediction. Each of these techniques is used in a different stage of the reasoning cycle of the case-based reasoning system to retrieve, to adapt and to review the proposed solution to the problem. This system has been used to predict the red tides that appear in the coastal waters of the north west of the Iberian Peninsula. The results obtained from those experiments are presented.


Radial Basis Function Fuzzy System Fuzzy Inference System Radial Basis Function Neural Network Radial Basis Function Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Nakhaeizadeh, G.: Learning prediction of time series. A theoretical and empirical comparison of CBR with some other approaches. In Proceedings of First European Workshop on Case-Based Reasoning, EWCBR-93. Kaiserslautern, Germany.(1993) 65–76Google Scholar
  2. 2.
    Lendaris, G. G., and Fraser, A. M.: Visual Fitting and Extrapolation. In Weigend, A. S., and Fershenfield, N. A. (Eds.). Time Series Prediction, Forecasting the Future and Understanding the Past. Addison Wesley. (1994) 35–46Google Scholar
  3. 3.
    Faltings, B.: Probabilistic Indexing for Case-Based Prediction. In Proceedings of Case-Based Reasoning Research and Development, Second International Conference, ICCBR-97. Provindence, Rhode Island, USA. (1997) 611–622Google Scholar
  4. 4.
    Lekkas, G. P., Arouris, N. M., Viras, L. L.: Case-Based Reasoning in Environmental Monitoring Applications. Artificial Intelligence, 8, (1994) 349–376Google Scholar
  5. 5.
    Mcintyre, H. S., Achabal, D. D., Miller, C. M.: Applying Case-Based Reasoning to Forecasting Retail Sales. Journal of Retailing, 69, num. 4, (1993) 372–398CrossRefGoogle Scholar
  6. 6.
    Stottler, R. H.: Case-Based Reasoning for Cost and Sales Prediction. AI Expert, (1994) 25–33Google Scholar
  7. 7.
    Weber-Lee, R., Barcia, R. M., and Khator, S. K.: Case-based reasoning for cash flow forecasting using fuzzy retrieval. In Proceedings of the First International Conference, ICCBR-95. Sesimbra, Portugal, (1995) 510–519Google Scholar
  8. 8.
    Fyfe C., and Corchado J. M.: Automating the construction of CBR Systems using Kernel Methods. International Journal of Intelligent Systems, 16, num. 4, (2001) 571–586zbMATHCrossRefGoogle Scholar
  9. 9.
    Corchado, J. M., and Lees, B.: Adaptation of Cases for Case-based Forecasting with Neural Network Support. In Pal, S. K., Dilon, T. S., and Yeung, D. S. (Eds.). Soft Computing in Case Based Reasoning. London: Springer Verlag, (2000) 293–319Google Scholar
  10. 10.
    Pal, S. K., Dilon, T. S., and Yeung, D. S.: Soft Computing in Case Based Reasoning. Springer Verlag: London, (2001)zbMATHGoogle Scholar
  11. 11.
    Corchado, J. M., Lees, B.: A Hybrid Case-based Model for Forecasting. Applied Artificial Intelligence, 15, num. 2, (2001) 105–127CrossRefGoogle Scholar
  12. 12.
    Corchado, J. M., Lees, B., Aiken, J.: Hybrid Instance-based System for Predicting Ocean Temperatures. International Journal of Computational Intelligence and Applications, 1, num. 1, (2001) 35–52CrossRefGoogle Scholar
  13. 13.
    Corchado, J. M., Aiken, J., Rees, N.: Artificial Intelligence Models for Oceanographic Forecasting. Plymouth Marine Laboratory, U.K., (2001)Google Scholar
  14. 14.
    Fritzke, B.: Growing Self-Organizing Networks-Why?. In Verleysen, M. (Ed.). European Symposium on Artificial Neural Networks, ESANN-96. Brussels, (1996) 61–72Google Scholar
  15. 15.
    Fritzke, B.: Fast learning with incremental RBF Networks. Neural Processing Letters, 1, num. 1, (1994) 2–5CrossRefGoogle Scholar
  16. 16.
    Jin, Y., Seelen, W. von., and Sendhoff, B.: Extracting Interpretable Fuzzy Rules from RBF Neural Networks. Internal Report IRINI 00-02, Institut für Neuroinfor-matik, Ruhr-Universität Bochum, Germany, (2000)Google Scholar
  17. 17.
    Fritzke, B.: Growing Cell Structures-A Self-organizing Network for Unsupervised and Supervised Learning. Technical Report, International Computer Science Institute. Berkeley, (1993)Google Scholar
  18. 18.
    Azuaje, F., Dubitzky, W., Black, N., and Adamson, K.: Discovering Relevance Knowledge in Data: A Growing Cell Structures Approach. IEEE Transactions on Systems, Man and Cybernetics, 30, (2000) 448–460CrossRefGoogle Scholar
  19. 19.
    Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, 15, (1985) 116–132zbMATHGoogle Scholar
  20. 20.
    Setnes, M., Babuska, R., Kaymak, U., and van Nauta, H. R.: Similarity measures in Fuzzy Rule Base Simplification. IEEE Transactions on systems, Man, and Cybernetics, 28, num. 3, (1998) 376–386CrossRefGoogle Scholar
  21. 21.
    Tomczak, M., Godfrey, J. S.: Regional Oceanographic: An Introduction. Pergamon, New York, (1994)Google Scholar
  22. 22.
    Fernández, E.: Las Mareas Rojas en las Rías Gallegas. Technical Report, Department of Ecology and Animal Biology. University of Vigo, (1998)Google Scholar
  23. 23.
    Hallegraeff, G. M.: A review of harmful algal blooms and their apparent global increase. Phycologia, 32, (1993) 79–99Google Scholar
  24. 24.
    Kamykowski, D.: The simulation of a southern California red tide using characteristics of a simultaneously-measured internal wave field. Ecol. Model., 12, (1981) 253–265CrossRefGoogle Scholar
  25. 25.
    Watanabe, M., Harashima, A.: Interaction between motile phytoplankton and Langmuir circulation. Ecol. Model., 31, (1986) 175–183CrossRefGoogle Scholar
  26. 26.
    Franks, P. J. S., Anderson, D. M.: Toxic phytoplankton blooms in the southwestern Gulf of Maine: testing hypotheses of physical control using historical data. Marine Biology, 112, (1992) 165–174CrossRefGoogle Scholar
  27. 27.
    Anderson, D. M.: Toxic algal blooms and red tides: a global perspective. In Okaichi, T., Anderson, D. M., and Nemoto, T. (Eds.). RedTides: Biology, Environmental Science and Toxicology. New York: Elsevier, (1989) 11–16Google Scholar
  28. 28.
    Corchado, J. M., Fyfe, C.: Unsupervised Neural Network for Temperature Forecasting. Artificial Intelligence in Engineering, 13, num. 4, (1999) 351–357CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Florentino Fdez-Riverola
    • 1
  • Juan M. Corchado
    • 2
  • Jesús M. Torres
    • 3
  1. 1.Dpto. de InformáticaE.S.E.I., University of VigoOurenseSpain
  2. 2.Dpto. de Informática y AutomáticaUniversity of SalamancaSalamancaSpain
  3. 3.Dpto. de Física AplicadaUniversity of VigoVigoSpain

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