A new method of piecewise linear approximation of non-stationary time series

Authors

  • Alexander MILNIKOV International Black Sea University
  • Cihan MERT International Black Sea University
  • Daniyar SATYBALDIEV International Ataturk Alatoo University

DOI:

https://doi.org/10.31578/.v4i1.66

Keywords:

Piecewise Polynomial Aggregate, support points, multiple nonlinear regressions

Abstract

Recently, in data science one of the most important issues has been discovering actionable information, interpretable patterns and relationships
in large volumes of data. This process is called data mining and is commonly being used in science, engineering, business and security.
One of the main methods of data mining is similarity search of time series. The approach that is discussed in this article is based on Piecewise
Linear Representation of time series that imply two steps of measuring time series similarity. A new method of piecewise linear approximation
of non-stationary time series is developed.

Author Biographies

Alexander MILNIKOV, International Black Sea University

Prof. Dr. Department of Computer Technologies and Engineering

Cihan MERT, International Black Sea University

Assoc. Prof. Dr., Faculty of Computer Technologies and Engineering

Daniyar SATYBALDIEV, International Ataturk Alatoo University

Department Electronics and Nano electronics

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Published

16-10-2015

Issue

Section

Articles