Generalized principal component analysis for moderately non-stationary vector time series

This paper extends the principal component analysis (PCA) to moderately non-stationary vector time series. We propose a method that searches for linear transformation of original series such transformed is segmented into uncorrelated subseries with lower dimensions. A columns’ rearrangement proposed regroup based on their relationships. discuss theoretical properties fixed and large dimensional cases. Many simulation studies show our approach suitable data. Illustrations real data are provided.