Data from: Posterior Singular Spectrum Analysis
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
A method is proposed for finding interesting underlying features of a time series, such as trends, maxima, minima and oscillations. A combination of Singular Spectrum Analysis (SSA) and Bayesian modeling is used where the credibility of SSA signal components are analyzed via posterior simulation. The potential of the technique is demonstrated using artificial and real data examples. Our analysis of a Bayesian re- construction of post Ice Age temperature variation lends support for the presence oscillations detected in previous studies of the paleoclimate.