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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具有自相关数据的主成分分析

本文有助于多元时间相关线性过程中主成分分析的分析，解释和使用。研究了忽略向量过程的自相关结构的效果。结果显示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多余颗粒物测量。在八个被考虑的监测站中，发现需要三个来表征该地区污染物的动态。