Transformation to approximate independence for locally stationary Gaussian processes
Transformation to approximate independence for locally stationary Gaussian processes
复制标题
局部平稳高斯过程近似独立的变换
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
M. Stein
中科院分区：
文献类型：
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作者：
J. Guinness;M. Stein
我们提供了新的近似下的局部平稳高斯过程模型的时间序列的可能性。即使在进化谱在重新标度的时域中不平滑的情况下，似然近似也是有效的。我们描述了一个广泛的一类模型的进化谱的近似可以计算得特别有效。在开发的近似，我们扩展到本地平稳的情况下的想法，离散傅立叶变换是一个去相关变换平稳的时间序列。这些近似用于将非平稳时间序列模型拟合到高频温度数据。对于这些数据，我们拟合在时间上分段恒定的进化谱，并使用遗传算法来搜索时间间隔的最佳划分。
We provide new approximations for the likelihood of a time series under the locally stationary Gaussian process model. The likelihood approximations are valid even in cases when the evolutionary spectrum is not smooth in the rescaled time domain. We describe a broad class of models for the evolutionary spectrum for which the approximations can be computed particularly efficiently. In developing the approximations, we extend to the locally stationary case the idea that the discrete Fourier transform is a decorrelating transformation for stationary time series. The approximations are applied to fit non‐stationary time‐series models to high‐frequency temperature data. For these data, we fit evolutionary spectra that are piecewise constant in time and use a genetic algorithm to search for the best partition of the time interval.