Abstract This paper extends the principal component analysis (PCA) to moderately non-stationary vector time series. We propose a method that searches for a linear transformation of the original series such that the transformed series is segmented into uncorrelated subseries with lower dimensions. A columns’ rearrangement method is proposed to regroup transformed series based on their relationships. We discuss the theoretical properties of the proposed method for fixed and large dimensional cases. Many simulation studies show our approach is suitable for moderately non-stationary data. Illustrations on real data are provided.

中文翻译：

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中等非平稳向量时间序列的广义主成分分析

摘要 本文将主成分分析（PCA）扩展到中等非平稳向量时间序列。我们提出了一种搜索原始序列的线性变换的方法，以便将变换后的序列分割成具有较低维度的不相关子序列。提出了一种列的重排方法，以根据它们的关系重新组合变换后的序列。我们讨论了所提出的方法在固定和大维情况下的理论特性。许多模拟研究表明我们的方法适用于中等非平稳数据。提供了真实数据的插图。