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A New Approach and Its Applications for Time Series Analysis and Prediction Based on Moving Average of nth-Order Difference

  • Yang Lan
  • Daniel Neagu
Part of the Intelligent Systems Reference Library book series (ISRL, volume 24)

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.

Keywords

Time Series Time Series Analysis Sunspot Number Mean Absolute Error Time Series Prediction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yang Lan
    • 1
  • Daniel Neagu
    • 1
  1. 1.Department of Computing, School of Computing, Informatics and MediaUniversity of BradfordBradfordUK

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