Grouping in Singular Spectrum Analysis of Time Series
Publication: Journal of Hydrologic Engineering
Volume 27, Issue 9
Abstract
Singular spectrum analysis (SSA) is a nonparametric model-free time-series analysis and filtering technique with a wide variety of applications in numerous data-intensive fields. The grouping stage is the most crucial step in SSA, where the analyst selects significant components from the time series for further processing. However, there is no universal rule in this stage of grouping and the components need to be grouped based on the data characteristics. In this study, a few methods that can be adopted for grouping are discussed and their efficiencies in reconstructing the time series are compared. The results of the study will be helpful in understanding the procedure and will act as a guide in the selection of a method for grouping based on the data characteristics. Real-world daily rainfall time-series data were used as a case study.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
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Published In
Journal of Hydrologic Engineering
Volume 27 • Issue 9 • September 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Mar 6, 2021
Accepted: May 11, 2022
Published online: Jul 5, 2022
Published in print: Sep 1, 2022
Discussion open until: Dec 5, 2022
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