Abstract 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 is analyzed via posterior simulation. The potential of the technique is demonstrated using artificial and real data examples. Our analysis of a Bayesian reconstruction of post-Ice Age temperature variation lends support for the pr.
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