An Exploratory Analysis of Multiple Multivariate Time Series

Our aim is to extend standard principal component analysis for non-time series data to explore and highlight the main structure of multiple sets of multivariate time series. To this end, standard variance-covariance matrices are generalized to lagged cross-autocorrelation matrices. The methodology produces principal component time series, which can be analysed in the usual way on a principal component plot, except that the plot also includes time as an additional dimension.

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