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A Wavelet Analysis Based Data Processing for Time Series of Data Mining Predicting

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Advances in Knowledge Discovery and Data Mining (PAKDD 2006)

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Abstract

This paper presents wavelet method for time series in business-field forecasting. An autoregressive moving average (ARMA) model is used, it can model the near-periodicity, nonstationarity and nonlinearity existed in business short-term time series. According to the wavelet denoising, wavelet decomposition and wavelet reconstruction, the hidden period and the nonstationarity existed in time series are extracted and separated by wavelet transformation. The characteristic of wavelet decomposition series is applied to BP networks and an autoregressive moving average (ARMA) model. It shows that the proposed method can provide more accurate results than the conventional techniques, like those only using BP networks or autoregressive moving average (ARMA) models.

Sponsored by National Natural Science Foundation of China (Grant No. 70501009).

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References

  1. Aggarwal, C.C., Yu, P.S.: Data mining techniques for associations, clustering and classification. In: Zhong, N., Zhou, L. (eds.) PAKDD 1999. LNCS, vol. 1574, pp. 13–23. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  2. Agrawal, D., Aggarwal, C.C.: On the design and quantification of privacy preserving data mining algorithms. In: PODS, pp. 247–255 (2001)

    Google Scholar 

  3. Christopher, J.C.B.: A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery (1998)

    Google Scholar 

  4. Nason, G.P., Sachs, R.V.: Wavelets in time- series analysis. Phil. Trans. R. Soc. Lond. A 357(1760), 2511–2526 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  5. Campbell, A.J., Murtagh, F.: Combining neural networks forecasts on wavelet transformed time series. Connection Sci. 9, 113–121 (1997)

    Article  Google Scholar 

  6. Jianze, W., Qiwen, R., Yaochao, J., Zhuo, L.: Frequency Domain Analysis of Wavelet Transform in Harmonics Detection. AEPS 22(7), 40–43 (1998)

    Google Scholar 

  7. Rahman, S., Bhatnagar, R.: An expert system based algorithm for short term load forecast. IEEE Trans. Power Systems 3(2), 392–399 (1988)

    Article  Google Scholar 

  8. Aiguo, S., Jiren, L.: Evolving Gaussian RBF network for nonlinear time series modelling and prediction. Electro- nics Lett. 34(12), 1241–1243 (1998)

    Article  Google Scholar 

  9. Chen, S.: Nonlinear time series modelling and prediction using Gaussian RBF network with enhanced clustering and RLS learning. Electron Lett. 31(2), 117–118 (1995)

    Article  Google Scholar 

  10. Yang, H.T., Huang, C.M.: A new short term load forecasting approach using self organizing fuzzy ARMAX models. IEEE Trans.Power Systems 13(1), 217–225 (1998)

    Article  Google Scholar 

  11. Geva, B.: Scale Net-Multiscale neural network architecture for time series prediction. IEEE Trans. Neural Networks 9, 1471–1482 (1998)

    Article  Google Scholar 

  12. Rahman, M.S.: Analysis and evaluation of five short term load forecasting techniques. IEEE Trans. Power Systems 5(4), 1484–1491 (1989)

    Google Scholar 

  13. Campbell, A., Murtagh, F.: Combining neural networks forecasts on wavelet transformed time series. Connection Sci. 9, 113–121 (1997)

    Article  Google Scholar 

  14. Amjady, N., Ehsan, M.: Transient stability assessment of power systems by a new estimating neural network. Can. J. Elect. & Comp. Eng. 22(3), 131–137 (1997)

    Article  Google Scholar 

  15. Hongwei, Z., Zhen, R., Weiying, H.: A Short Load Forecasting Method Based on PAR Model. Proceedings of the CSEE 17(5), 348–351 (1997)

    Google Scholar 

  16. Rahman, S.: Generalized knowledge-based short- term load forecasting technique. IEEE Trans. Power Systems 8(2), 508–514 (1993)

    Article  Google Scholar 

  17. Mallat, S.G.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. PAMI 11, 674–693 (1989)

    Article  MATH  Google Scholar 

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© 2006 Springer-Verlag Berlin Heidelberg

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Tong, W., Li, Y., Ye, Q. (2006). A Wavelet Analysis Based Data Processing for Time Series of Data Mining Predicting. In: Ng, WK., Kitsuregawa, M., Li, J., Chang, K. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2006. Lecture Notes in Computer Science(), vol 3918. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11731139_91

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  • DOI: https://doi.org/10.1007/11731139_91

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33206-0

  • Online ISBN: 978-3-540-33207-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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