Singular spectrum analysis for time series with missing data

D. H. Schoellhamer

doi:10.1029/2000gl012698

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Singular spectrum analysis for time series with missing data

D. H. Schoellhamer

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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.

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Geophysical Research Letters Article

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D. H. Schoellhamer. 2001-08-15. Singular spectrum analysis for time series with missing data. https://doi.org/10.1029/2000gl012698

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