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Piecewise evolutionary segmentation for feature extraction in time series models

Abstract

The design, development and implementation of an innovative system utilized in feature extraction from time series data models is described in this manuscript. Achieving to design piecewise segmentation patterns on the time series in an evolutionary fashion and use them in order to produce fitter secondary data sets, the developed system adapts itself to the nature of the problem each time and finally elects an optimally parameterized classifier (artificial neural network or support vector machine), along with the fittest time series segmentation pattern. The application of the system onto two different problems involving time series data analysis and requiring predictive and classification capabilities (torrential risk assessment and plant virus identification, respectively), reveals that the proposed methodology was crucial in finding the optimum solution for both problems. Piecewise evolutionary segmentation time series model analysis, utilized by the accompanying software tool, succeeded in controlling the dimensionality and noise inherent in the initial raw time series information. The process eventually proposes a segmentation pattern for each problem, enhancing the potential of the corresponding classifier.

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Correspondence to Thomas J. Glezakos.

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Glezakos, T.J., Tsiligiridis, T.A. & Yialouris, C.P. Piecewise evolutionary segmentation for feature extraction in time series models. Neural Comput & Applic 24, 243–257 (2014). https://doi.org/10.1007/s00521-012-1212-y

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Keywords

  • Evolutionary computing
  • Machine learning
  • Artificial neural networks
  • Support vector machines
  • Plant virus identification
  • Torrential risk management