Singular spectrum analysis for time series with missing data
January 1, 2001
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.
Citation Information
| Publication Year | 2001 |
|---|---|
| Title | Singular spectrum analysis for time series with missing data |
| DOI | 10.1029/2000GL012698 |
| Authors | D. H. Schoellhamer |
| Publication Type | Article |
| Publication Subtype | Journal Article |
| Series Title | Geophysical Research Letters |
| Index ID | 70023264 |
| Record Source | USGS Publications Warehouse |
| USGS Organization | San Francisco Bay-Delta; Pacific Regional Director's Office |