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Algebraic segmentation of short nonstationary time series based on evolutionary prediction algorithms

机译：基于进化预测算法的短非平稳时间序列的代数分割

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摘要

Algebraic segmentation of short nonstationary time series is presented in this paper. The proposed algorithm is based on the algebraic one step-forward predictor which is used to identify a temporal near-optimal algebraic model of the real-world time series. A combinatorial algorithm is used to identify intervals where prediction errors are lower than a predefined level of acceptable accuracy. Special deterministic strategy is developed for the selection of this acceptable level of prediction accuracy and is individually determined for every time series. The nonparametric identification of quasistationary segments is performed without the employment of any statistical estimator. Several standard real-world time series are used to demonstrate the efficiency of the proposed technique.
机译：本文提出了短非平稳时间序列的代数分割方法。所提出的算法是基于代数一阶预测器的，该预测器用于识别现实世界时间序列的时间近最优代数模型。组合算法用于识别预测误差低于可接受精度的预定义水平的区间。开发了特殊的确定性策略来选择可接受的预测精度水平，并针对每个时间序列分别确定。无需使用任何统计估计量即可对准平稳段进行非参数识别。几个标准的现实世界时间序列用于证明所提出的技术的效率。

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  • 来源
    《Neurocomputing》 |2013年第9期|354-364|共11页
  • 作者

    Rita Palivonaite; Kristina Lukoseviciute; Minvydas Ragulskis;

  • 作者单位

    Research Group for Mathematical and Numerical Analysis of Dynamical Systems, Kaunas University of Technology, Studentu 50-222, Kaunas LT-51368, Lithuania;

    Research Group for Mathematical and Numerical Analysis of Dynamical Systems, Kaunas University of Technology, Studentu 50-222, Kaunas LT-51368, Lithuania;

    Research Group for Mathematical and Numerical Analysis of Dynamical Systems, Kaunas University of Technology, Studentu 50-222, Kaunas LT-51368, Lithuania;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Segmentation; Time series prediction; Rank of a sequence;

    机译：分割;时间序列预测;序列的等级;

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