Singular spectrum analysis for time series with missing data
Geophysical Research Letters
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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.
| 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 |
| Contributing office(s) | San Francisco Bay-Delta, Pacific Regional Director's Office |
| Larger Work Type | Article |
| Larger Work Subtype | Journal Article |
| Larger Work Title | Geophysical Research Letters |
| First page | 3187 |
| Last page | 3190 |
| Online Only (Y/N) | N |
| Additional Online Files (Y/N) | N |
| Google Analytic Metrics | Metrics page |