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.
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Hathaway, D.H., Wilson, R.M., Reichmann, E.J.: The Shape of the Sunspot Cycle. Solar Physics 151, 177–190 (1994)
Calvo, R.A., Ceccatto, H.A., Piacentini, R.D.: Neural Network Prediction of Solar Activity. The Astrophysical Journal 444(2), 916–921 (1995)
Box, G., Jenkins, F.M.: Time Series Analysis: Forecasting and Control, 2nd edn. Holden-Day, Oakland, CA (1976)
Van Golub, L.: Matrix Computations, 3rd edn. Johns Hopkins University Press, Baltimore, MD (1996)
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)
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)
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)
National Geophysical Data Center (NGDC) (2006), http://www.ngdc.noaa.gov/
Wikipedia (2006), http://en.wikipedia.org/wiki
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© 2007 Springer Berlin Heidelberg
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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
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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
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