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Applications of the Moving Average of nth -Order Difference Algorithm for Time Series Prediction

  • Yang Lan
  • Daniel Neagu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4632)

Abstract

Currently, as a typical problem in data mining, Times Series Analysis and Prediction are facing continuously more applications on a wide variety of domains. Huge data collections are generated or updated from science, military, financial and environmental applications. Prediction of the future trends based on previous and existing values is of a high importance and various machine learning algorithms have been proposed. In this paper we discuss results of a new approach based on the moving average of the n th -order difference of limited range margin series terms. Based on our original approach, a new algorithm has been developed: performances on measurement records of sunspots for more than 200 years are reported and discussed. Finally, Artificial Neural Networks (ANN) are added for improving the precision of prediction by addressing the error of prediction in the initial approach.

Keywords

Time Series Analysis Pseudo-periodical Time Series Prediction nth-order Difference 

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References

  1. 1.
    Hathaway, D.H., Wilson, R.M., Reichmann, E.J.: The Shape of the Sunspot Cycle. Solar Physics 151, 177–190 (1994)CrossRefGoogle Scholar
  2. 2.
    Calvo, R.A., Ceccatto, H.A., Piacentini, R.D.: Neural Network Prediction of Solar Activity. The Astrophysical Journal 444(2), 916–921 (1995)CrossRefGoogle Scholar
  3. 3.
    Box, G., Jenkins, F.M.: Time Series Analysis: Forecasting and Control, 2nd edn. Holden-Day, Oakland, CA (1976)zbMATHGoogle Scholar
  4. 4.
    Van Golub, L.: Matrix Computations, 3rd edn. Johns Hopkins University Press, Baltimore, MD (1996)zbMATHGoogle Scholar
  5. 5.
    Simon, G., Lendasse, A., Cottrell, M., Fort, J.C., Verleysen, M.: Time series forecasting: Obtaining long term trends with self-organizing maps. Pattern Recognition Letters 26, 1795–1808 (2005)CrossRefGoogle Scholar
  6. 6.
    Saad, E.W., Prokhorov, D.V., Wunsch II, D.C.: Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks. IEEE Transactions on Neural Networks 9(6), 1456–1470 (1998)CrossRefGoogle Scholar
  7. 7.
    Lee Giles, C., Steve, L., Tsoi, A.C.: Noisy Time Series Prediction using a Recurrent Neural Network and Grammatical Inference. Machine Learning 44(1/2), 161–183 (2001)CrossRefzbMATHGoogle Scholar
  8. 8.
    National Geophysical Data Center (NGDC) (2006), http://www.ngdc.noaa.gov/
  9. 9.
    Wikipedia (2006), http://en.wikipedia.org/wiki

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Yang Lan
    • 1
  • Daniel Neagu
    • 1
  1. 1.Department of Computing, University of Bradford, Bradford, BD7 1DPUK

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