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A New ANFIS Synthesis Approach for Time Series Forecasting

  • Massimo Panella
  • Fabio Massimo Frattale Mascioli
  • Antonello Rizzi
  • Giuseppe Martinelli
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 18)

Abstract

ANFIS networks are neural models particularly suited to the solution of time series forecasting problems, which can be considered as function approximation problems whose inputs are determined by using past samples of the sequence to be predicted. In this context, clustering procedures represent a straightforward approach to the synthesis of ANFIS networks. The use of a clustering procedure, working in the conjunct input-output space of data, is proposed in the paper. Simulation tests and comparisons with other prediction techniques are discussed for validating the proposed synthesis approach. In particular, we consider the prediction of environmental data sequences, which are often characterized by a chaotic behavior. Consequently, well-known embedding techniques are used for solving the forecasting problems by means of ANFIS networks.

Keywords

Average Mutual Information Normalize Mean Square Error Chaotic Sequence Time Series Forecast Electric Power Consumption 
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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References

  1. 1.
    Jong, J.S., Sun, C.T., Mizutani, E.: Neuro-Fuzzy and Soft Computing: a Computational Approach to Learning and Machine Intelligence. Prentice Hall, Upper Saddle River, NJ USA (1997)Google Scholar
  2. 2.
    Frattale Mascioli, F.M., Mancini, A., Rizzi, A., Panella, M., Martinelli, G.: Neurofuzzy Approximator based on Mamdani’s Model. Proc. of WIRN2001, Vietri Sul Mare, Salerno, Italy (2001)Google Scholar
  3. 3.
    Panella, M., Rizzi, A., Frattale Mascioli, F.M., Martinelli, G.: ANFIS Synthesis by Hyperplane Clustering. Proc. of IFSA/NAFIPS 2001, Vancouver, Canada (2001)Google Scholar
  4. 4.
    Simpson, P.K.: Fuzzy Min-Max Neural Networks—Part 1: Classification. IEEE Transactions on Neural Networks, Vol. 3, No. 5 (1992) 776–786CrossRefGoogle Scholar
  5. 5.
    Rizzi, A., Panella, M., Frattale Mascioli, F.M.: Adaptive Resolution Min-Max Classifiers. To appear in IEEE Transactions on Neural Networks (2001–2002)Google Scholar
  6. 6.
    Haykin, S.: Neural Networks, A Comprehensive Foundation. Macmillan, New York, NY (1994)Google Scholar
  7. 7.
    Masulli, F., Studer, L.: Time Series Forecasting and Neural Networks. Invited tutorial in Proc. of IJCNN’99, Washington D.C., USA (1999)Google Scholar
  8. 8.
    Abarbanel, H.D.I.: Analysis of Observed Chaotic Data. Springer-Verlag, Berlin Heidelberg New York (1996)MATHCrossRefGoogle Scholar
  9. 9.
    Chiu, S.: Fuzzy Model Identification Based on Cluster Estimation. Journal of Intelligent & Fuzzy Systems, Vol. 2, No. 3 (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Massimo Panella
    • 1
  • Fabio Massimo Frattale Mascioli
    • 1
  • Antonello Rizzi
    • 1
  • Giuseppe Martinelli
    • 1
  1. 1.INFO-COM DepartmentUniversity of Rome “La Sapienza”RomeItaly

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