Some contributions to PCA for time series

Multidimensional time series appear in many fields of application. Sometimes, it can be useful to use PCA to reach dimensionality reduction. However, formal inference procedures on PC rely on the independence of the variables. Therefore, several PC-like techniques, as Singular Spectrum Analysis, are used to attain this reduction by decomposing the original series into a sum of a small number of interpretable components. Here, SSA and its extension are described and applied to real datasets.