This paper contributes to the analysis, interpretation and the use of the principal component analysis in a multivariate time-correlated linear process. The effect of ignoring the autocorrelation structure of the vector process is investigated. The results show a spurious impact of the time-correlation on the eigenvalues. To mitigate this impact, a pre-filtering procedure to whiten the data is applied. The methodology is used to identify redundant particulate matter measurements in a region in Brazil. Among the eight considered monitoring stations, it is found that three are needed to characterize the dynamic of the pollutant in the region.

中文翻译：

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使用自相关数据进行主成分分析

本文有助于在多元时间相关线性过程中分析、解释和使用主成分分析。研究了忽略向量过程的自相关结构的影响。结果显示了时间相关性对特征值的虚假影响。为了减轻这种影响，应用了预过滤程序来白化数据。该方法用于识别巴西某个地区的冗余颗粒物测量值。在考虑的八个监测站中，发现需要三个来表征该地区污染物的动态。