Abstract
High-dimensional and time-dependent data pose significant challenges to statistical process monitoring. Dynamic principal-component analysis, recursive principal-component analysis, and moving-window principal-component analysis have been proposed to cope with high-dimensional and time-dependent features. We present a comprehensive review of this literature for the practitioner encountering this topic for the first time. We detail the implementation of the aforementioned methods and direct the reader toward extensions that may be useful to their specific problem. A real-data example is presented to help the reader draw connections between the methods and the behavior they display. Furthermore, we highlight several challenges that remain for research in this area.
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