Abstract
As a typical problem in data mining, Time Series Predictions are widely applied in various domains. The approach focuses on series of observations, with the aim that, using mathematics, statistics and artificial intelligence methods, to analyze, process and make a prediction on the next most probable value based on a number of previous values. We propose an algorithm using the average sum of n th -order difference of series terms with limited range margins, in order to establish a way to predict the next series term based on both, the original data set and a negligible error. The algorithm performances are evaluated using measurement data sets on monthly average Sunspot Number, Earthquakes and Pseudo-Periodical Synthetic Time Series.
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Lan, Y., Neagu, D. (2012). A New Approach and Its Applications for Time Series Analysis and Prediction Based on Moving Average of n th-Order Difference. In: Holmes, D.E., Jain, L.C. (eds) Data Mining: Foundations and Intelligent Paradigms. Intelligent Systems Reference Library, vol 24. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23241-1_8
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DOI: https://doi.org/10.1007/978-3-642-23241-1_8
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