Classification of process dynamics with monte carlo singular spectrum analysis

Singular spectrum analysis is a linear multivariate method for the analysis of time series data, based on principal component analysis of an augmented data set consisting of the original time series data and lagged copies of the data. It can be used to decompose the time series into a set of component time series, each of which could be investigated individually to gain a better understanding of the process dynamics, or to allow for the removal of noise from the data.