Skip to main content

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

  • Conference paper
Advanced Data Mining and Applications (ADMA 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4632))

Included in the following conference series:

  • 2218 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Hathaway, D.H., Wilson, R.M., Reichmann, E.J.: The Shape of the Sunspot Cycle. Solar Physics 151, 177–190 (1994)

    Article  Google Scholar 

  2. Calvo, R.A., Ceccatto, H.A., Piacentini, R.D.: Neural Network Prediction of Solar Activity. The Astrophysical Journal 444(2), 916–921 (1995)

    Article  Google Scholar 

  3. Box, G., Jenkins, F.M.: Time Series Analysis: Forecasting and Control, 2nd edn. Holden-Day, Oakland, CA (1976)

    MATH  Google Scholar 

  4. Van Golub, L.: Matrix Computations, 3rd edn. Johns Hopkins University Press, Baltimore, MD (1996)

    MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  8. National Geophysical Data Center (NGDC) (2006), http://www.ngdc.noaa.gov/

  9. Wikipedia (2006), http://en.wikipedia.org/wiki

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer Berlin Heidelberg

About this paper

Cite this paper

Lan, Y., Neagu, D. (2007). Applications of the Moving Average of n th -Order Difference Algorithm for Time Series Prediction. In: Alhajj, R., Gao, H., Li, J., Li, X., Zaïane, O.R. (eds) Advanced Data Mining and Applications. ADMA 2007. Lecture Notes in Computer Science(), vol 4632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73871-8_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-73871-8_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73870-1

  • Online ISBN: 978-3-540-73871-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics