'Piecewise-linear approximation in solving problems of retrieving data' Software systems and computational methods nbpublish.com
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Sidorkina I.G. Shumkov D.S Piecewise-linear approximation in solving problems of retrieving data

Published in journal "Software systems and computational methods", 2013-2 in rubric "Mathematical models and computer simulation experiment", pages 171-175.

Resume: forecasting is one of the main issues in the analysis of the time series. The goal is to determine future behavior of a time sires by its know past values. In this article the author proposes a method of time series prediction based on the idea of allocating basic patters (templates) from the input data allowing to define the internal rules of the studied series. Currently on of the approaches in the field of time series prediction is the Data Mining (or data excavation) system. This is due to the fact that classical methods, based only on the linear (ARIMA) and non-linear (GARCH) models of prediction cant provide the required accuracy. Usage of the methods developed with this technology makes it possible to increase the performance of prediction and reveal hidden patterns in the studied time series.

Keywords: Software, time series, approximation, piecewise-linear approximation, forecasting, data excavation, basic patterns, patterns, local extremes, algorithm.

DOI: 10.7256/2305-6061.2013.2.7943

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1. Last M., Kandle A, Bunke H. Data Mining in Time Series Databases. Series in Machine Perception and Artificial
Intelligence. Vol. 57. World Scientific, 2004. 205 p.
2. Witten, I.H. (Ian H.) Data mining: practical machine learning tools and techniques / Ian H. Witten, Eibe
Frank. 2nd ed. Elsevier, 2005. 558 p.
3. Keogh, E.J., Chakrabarti, K., Mehrotra, S., and Pazzani, M.J. (2001a). Locally Adaptive Dimensionality Reduction
for Indexing Large Time Series Databases. Proc. 2001 ACM SIGMOD Conf. on Management of
Data. pp. 151162.
4. Kim, S., Park, S., and Chu, W.W. (2001). An Index-Based Approach for Similarity Search Supporting Time
Warping in Large Sequence Databases. Proc. 17th Int. Conf. on Data Engineering (ICDE). pp. 607614.
5. Dyuk V. Data Mining: uchebnyy kurs (+ CD). SPb: Piter, 2001. 386 s.
6. Lebedev E.K. Vychislenie veroyatnostey perekhodov dlya tsepey Markova, approksimiruyushchikh signa-
ly v fazovykh sistemakh / E.K. Lebedev, N.A. Galanina, N.N. Ivanova // Vestnik Chuvashskogo univer-
siteta. 2001. 3. S. 89-100.

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