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A two-level algorithm of time series change detection based on a unique changes similarity method

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2 Author(s)
Tomasz Pełech-Pilichowski ; AGH University of Science and Technology, Department of Applied Computer Science, Faculty of Management, ul. Gramatyka 10, 30-067 Krakow, Poland ; Jan T. Duda

In the paper, a novel two level algorithm of time series change detection is presented. In the first level, to identify non-stationary sequences in processed signals preliminary detection of events is performed with short-term prediction comparison. In the second stage, to confirm changes detected in first level a unique changes similarity method is employed. Detection of changes in non-stationary time series is discussed, implemented algorithms are described and results produced on exemplary four financial time series are showed.

Published in:

Computer Science and Information Technology (IMCSIT), Proceedings of the 2010 International Multiconference on

Date of Conference:

18-20 Oct. 2010