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International Conference on Artificial Neural Networks

ICANN 2006: Artificial Neural Networks – ICANN 2006 pp 191–200Cite as

Time Series Prediction Using Fuzzy Wavelet Neural Network Model

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4132))

Abstract

The fuzzy wavelet neural network (FWNN) for time series prediction is presented in this paper. Using wavelets the fuzzy rules are constructed. The gradient algorithm is applied for learning parameters of fuzzy system. The application of FWNN for modelling and prediction of complex time series and prediction of electricity consumption is considered. Results of simulation of FWNN based prediction system is compared with the simulation results of other methodologies used for prediction. Simulation results demonstrate that FWNN based system can effectively learn complex nonlinear processes and has better performance than other models.

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References

  1. Box, G.E.P., Jenkins, G.M., Reinsel, G.C.: Time-series analysis, Forecasting and Control, 3rd edn. Prentice-Hall, Inc, Englewood Cliffs (1994)

    MATH  Google Scholar 

  2. Maddala, G.S.: Introduction to econometrics. Prentece-Hall, Englewood Cliffs (1996)

    Google Scholar 

  3. Smaoui, N.: An Artificial Neural Network Noise Reduction Method for Chaotic Attractors. Intern J. Computer Math. 73, 417–431 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  4. Crowder III, R.S.: Predicting the Mackey-Glass time series with cascade correlation learning. In: Touretzky, D., Hinton, G., Sejnowski, T. (eds.) Proceeding of the Connectionist Models Summer School, pp. 117–123. Carnegic Mellon University, Pittsburgh (1990)

    Google Scholar 

  5. Nunnari, G., Nucifora, A., Randieri, C.: The application of neural techniques to the modeling of time series of atmospheric pollution data. Ecological Modelling 111, 187–205 (1998)

    Article  Google Scholar 

  6. Hill, T., O’Connor, M., Remus, W.: Artificial neural network models for forecasting and decision making. Int. Journal of Forecasting 10, 5–15 (1994)

    Article  Google Scholar 

  7. Tang, Z., de Almeida, C., Fishwick, P.A.: Time-series forecasting using neural network versus Box-Jenkins methodology. Simulation 57, 303–310 (1991)

    Article  Google Scholar 

  8. Kugarajah, T., Zhang, Q.: Multidimensional wavelet frames. IEEE Transaction on Neural Networks 6, 1552–1556 (1995)

    Article  Google Scholar 

  9. Szu, H., Telfer, B., Garcia, J.: Wavelet Transforms and Neural Networks for Compression and recognition. Neural Networks 9, 695–708 (1996)

    Article  Google Scholar 

  10. Chui, C.K.: An Introduction to Wavelets. Academic Press, New York (1992)

    MATH  Google Scholar 

  11. Cheng, Y., Chen, B., Shiau, F.: Adaptive Wavelet Network Control Design for Nonlinear Systems. Proc. Natl. Sci. Counc. ROC(A) 22(6), 783–799 (1998)

    Google Scholar 

  12. Chang, P.R., Fu, W., Yi, M.: Short term load forecasting using wavelet networks. Engineering Intel.Syst. for Electrical Engineering and Communications 6, 217–223 (1998)

    Google Scholar 

  13. Cao, L., Hong, Y., Fang, H., He, G.: Predicting Chaotic time-series with wavelet networks. Physica D, 225–238 (1995)

    Google Scholar 

  14. Abdel-Aal, R.E., Al-Garni, A.Z., Al-Nassar, Y.N.: Modelling and forecasting monthly electricity consumption in eastern Saudi Arabia using abductive networks. Energy 22 (1997)

    Google Scholar 

  15. Yan, Y.Y.: Climate and residential electricity consumption in Hong Kong. Energy 23(1), 17–20 (1998)

    Article  Google Scholar 

  16. Rajan, M., Jain, V.K.: Modelling of electrical energy consumption in Delhi. Energy 24 (1999)

    Google Scholar 

  17. Thuillard, M.: Fuzzy logic in the wavelet framework. In: Proc. Toolmet 2000, Oulu (2000)

    Google Scholar 

  18. Abiyev, R.H.: Controller Based of Fuzzy Wavelet Neural Network for Control of Technological Processes. In: IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, Giardini Naxos, Italy (2005)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Near East University, Lefkosa, North Cyprus

    Rahib H. Abiyev

Authors
  1. Rahib H. Abiyev
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Editor information

Editors and Affiliations

  1. School of Electrical and Computer Engineering, Image, Video and Multimedia Systems Laboratory, National Technical University of Athens, 157 80, Zographou, GR, Greece

    Stefanos Kollias

  2. Department of Electrical and Computer Engineering, National Technical University of Athens, 15780, Zographou, Greece

    Andreas Stafylopatis

  3. Department of Informatics, Nicolaus Copernicus University, Toruń, Poland

    Włodzisław Duch

  4. Adaptive Informatics Research Centre, Helsinki University of Technology, P.O. Box 5400, 02015, HUT, Finland

    Erkki Oja

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© 2006 Springer-Verlag Berlin Heidelberg

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Abiyev, R.H. (2006). Time Series Prediction Using Fuzzy Wavelet Neural Network Model. In: Kollias, S., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840930_20

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  • DOI: https://doi.org/10.1007/11840930_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-38871-5

  • Online ISBN: 978-3-540-38873-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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