A new algorithm in singular spectrum analysis framework: The Overlap-SSA (ov-SSA)

The Singular Spectrum Analysis (SSA) is powerful method, capable of working with arbitrary statistical process and it is adaptive to the underlaying data. Many variations of the standard methodology have been prosed in recent years improving the performance, adjusting to specific problems or objectives, or addressing some shortcomings. One of such drawbacks occurs when the spectrum spreads and varies over time, demanding many elementary matrices to reconstruct an approximation of the original series, hampering the method applicability. Also, another difficulty arises when large datasets are analyzed. There are computational issues and also problems with the method ability to maintain satisfactory separability. To circumvent these issues, a new method has been proposed. The original time series is divided into smaller and consecutive segments, with some superposition between them. Then, standard SSA is applied to each segment and the results are concatenated properly. This paper provides an implementation of this algorithm and some experiments are shown to illustrate the improvements achieved. (C) 2017 The Authors. Published by Elsevier B.V.