Detecting structural breaks in time series via genetic algorithms

作者： | Benjamin Doerr, Paul Fischer, Astrid Hilbert, Carsten Witt |

作者单位： |
^{1}École Polytechnique^{2}DTU Compute Technical University of Denmark^{3}Mathematics Linnaeus University |

刊名： | Soft Computing, 2017, Vol.21 (16), pp.4707-4720 |

来源数据库： | Springer Nature Journal |

DOI： | 10.1007/s00500-016-2079-0 |

关键词： | Genetic Algorithms; Statistics; Break points; Experimentation; Time series; Range trees; |

英文摘要： | Detecting structural breaks is an essential task for the statistical analysis of time series, for example, for fitting parametric models to it. In short, structural breaks are points in time at which the behaviour of the time series substantially changes. Typically, no solid background knowledge of the time series under consideration is available. Therefore, a black-box optimization approach is our method of choice for detecting structural breaks. We describe a genetic algorithm framework which easily adapts to a large number of statistical settings. To evaluate the usefulness of different crossover and mutation operations for this problem, we conduct extensive experiments to determine good choices for the parameters and operators of the genetic algorithm. One surprising observation is... |

原始语种摘要： | Detecting structural breaks is an essential task for the statistical analysis of time series, for example, for fitting parametric models to it. In short, structural breaks are points in time at which the behaviour of the time series substantially changes. Typically, no solid background knowledge of the time series under consideration is available. Therefore, a black-box optimization approach is our method of choice for detecting structural breaks. We describe a genetic algorithm framework which easily adapts to a large number of statistical settings. To evaluate the usefulness of different crossover and mutation operations for this problem, we conduct extensive experiments to determine good choices for the parameters and operators of the genetic algorithm. One surprising observation is... |