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We report on the results of two new approaches to considering how manyprincipal components to retain from an analysis of a multivariate time series.The first is by using a "heat map" based approach. A heat map in this contextrefers to a series of principal component coefficients created by applying asliding window to a multivariate time series. Furthermore the heat maps canprovide detailed insights into the evolution of the structure of each principalcomponent over time. The second is by examining the change of the angle of theprincipal component over time within the high-dimensional data space. Weprovide evidence that both are useful in studying structure and evolution of amultivariate time series.

How Many Components should be Retained from a Multivariate Time Series PCA?