Singular spectrum analysis for time series with missing data

Geophysical time series often contain missing data, which prevents analysis with many signal processing and multivariate tools. A modification of singular spectrum analysis for time series with missing data is developed and successfully tested with synthetic and actual incomplete time series of susp...

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Bibliographic Details
Published in:Geophysical research letters 2001-08, Vol.28 (16), p.3187-3190
Main Author: Schoellhamer, David H.
Format: Article
Language:English
Subjects:
Earth, ocean, space
Exact sciences and technology
External geophysics
Geophysics. Techniques, methods, instrumentation and models
Other topics
Physics of the oceans
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Summary:Geophysical time series often contain missing data, which prevents analysis with many signal processing and multivariate tools. A modification of singular spectrum analysis for time series with missing data is developed and successfully tested with synthetic and actual incomplete time series of suspended‐sediment concentration from San Francisco Bay. This method also can be used to low pass filter incomplete time series.
ISSN:0094-8276
1944-8007
DOI:10.1029/2000GL012698