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首页> 外文期刊>Journal of Time Series Analysis >Time-varying multi-regime models fitting by genetic algorithms
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Time-varying multi-regime models fitting by genetic algorithms

机译：遗传算法拟合的时变多区域模型

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摘要

Many time series exhibit both nonlinearity and non-stationarity. Though both features have been often taken into account separately, few attempts have been proposed for modelling them simultaneously. We consider threshold models, and present a general model allowing for different regimes both in time and in levels, where regime transitions may happen according to self-exciting, or smoothly varying or piecewise linear threshold modelling. Since fitting such a model involves the choice of a large number of structural parameters, we propose a procedure based on genetic algorithms, evaluating models by means of a generalized identification criterion. The performance of the proposed procedure is illustrated with a simulation study and applications to some real data.
机译：许多时间序列同时表现出非线性和非平稳性。尽管经常单独考虑这两个功能，但很少有人提出同时建模的尝试。我们考虑阈值模型，并提出了一个通用模型，该模型允许在时间和级别上采用不同的体制，其中体制转变可能会根据自激，平稳变化或分段线性阈值建模而发生。由于拟合这样的模型需要选择大量的结构参数，因此我们提出了一种基于遗传算法的程序，并通过广义识别准则对模型进行评估。通过仿真研究和对某些实际数据的应用说明了所建议程序的性能。

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