Advertisement

A Computational Intelligence Approach for Forecasting Telecommunications Time Series

  • Paris A. Mastorocostas
  • Constantinos S. Hilas
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 151)

Abstract

In this work a computational intelligence-based approach is proposed for forecasting outgoing telephone calls in a University Campus. A modified Takagi–Sugeno-Kang fuzzy neural system is presented, where the consequent parts of the fuzzy rules are neural networks with internal recurrence, thus introducing dynamics to the overall system. The proposed model, entitled Locally Recurrent Neurofuzzy Forecasting System (LR-NFFS), is compared to well-established forecasting models, where its particular characteristics are highlighted.

Keywords

Fuzzy Rule Mean Absolute Percentage Error Recurrent Neural Network Exponential Smoothing Consequent Part 
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.

References

  1. 1.
    Hilas CS, Goudos SK, Sahalos JN (2006) Seasonal decomposition and forecasting of telecommunication data: A comparative case study. Technol Forecast Soc Change 73(5):495–509CrossRefGoogle Scholar
  2. 2.
    Madden G, Joachim T (2007) Forecasting telecommunications data with linear models. Telecommun Policy, 31(1):31–44CrossRefGoogle Scholar
  3. 3.
    Makridakis, SG, Wheelwright SC, McGee, VE (1998) Forecasting: methods and applications. 3d ed., Wiley, New YorkGoogle Scholar
  4. 4.
    Holt CE (2004) Forecasting trends and seasonals by exponentially weighted moving averages ONR Memorandum 52, Carnegie Institute of Technology, Pittsburg, (1957). (Reprinted: Int J Forecasting, 20:5–10)Google Scholar
  5. 5.
    Winters PR (1960) Forecasting sales by exponentially weighted moving averages. Manag Sci, 6:324-342MathSciNetMATHCrossRefGoogle Scholar
  6. 6.
    Gardner ES Jr (1985) Exponential smoothing: the state of the art. J Forecast 4:1–28CrossRefGoogle Scholar
  7. 7.
    Box GEP, Jenkins, G M (1976) Time series analysis: forecasting and control. 2nd ed., Holden-Day, San FranciscoMATHGoogle Scholar
  8. 8.
    Takagi, T Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst, Man, and Cybern, Man, and Cybernetics 15:116–132MATHCrossRefGoogle Scholar
  9. 9.
    Tsoi AC Back AD (1994) Locally recurrent globally feedforward networks: a critical review of architectures. IEEE Trans. Neural Networks 5:229–239CrossRefGoogle Scholar
  10. 10.
    Mastorocostas PA, Theocharis JB (2002) A recurrent fuzzy neural model for dynamic system identification. IEEE Trans Syst Man, and Cybern: Part 32:176–190CrossRefGoogle Scholar
  11. 11.
    Mastorocostas PA (2004) Resilient back propagation learning algorithm for recurrent fuzzy neural networksElectron Lett, 40:57–58CrossRefGoogle Scholar
  12. 12.
    Riedmiller M, Braun H (1993) A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: proceeding IEEE International Joint Conference on Neural Networks, pp 586–591Google Scholar
  13. 13.
    Werbos PJ (1974) Beyond regression: New tools for prediction and analysis in the behavioral sciences, Ph.D. Thesis, Harvard University, CambridgeGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Paris A. Mastorocostas
    • 1
  • Constantinos S. Hilas
    • 1
  1. 1.Department of Informatics and CommunicationsTechnological Educational Institute of SerresSerresGreece

Personalised recommendations