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Singular spectrum analysis for time series with missing data

Geophysical Research Letters

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DOI: 10.1029/2000GL012698

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Abstract

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.

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Singular spectrum analysis for time series with missing data
Series title:
Geophysical Research Letters
DOI:
10.1029/2000GL012698
Volume
28
Issue:
16
Year Published:
2001
Language:
English
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
Larger Work Title:
Geophysical Research Letters
First page:
3187
Last page:
3190
Number of Pages:
4