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Singular spectrum analysis (SSA) is a powerful technique fortime series analysis. Based on the property that the original time seriescan be reproduced from its principal components, this contribution developsan improved SSA (ISSA) for processing the incomplete time series and themodified SSA (SSAM) of Schoellhamer (2001) is its special case. The approach is evaluated with the synthetic and real incomplete time series data ofsuspended-sediment concentration from San Francisco Bay. The result from thesynthetic time series with missing data shows that the relative errors ofthe principal components reconstructed by ISSA are much smaller than thosereconstructed by SSAM. Moreover, when the percentage of the missing dataover the whole time series reaches 60 %, the improvements of relativeerrors are up to 19.64, 41.34, 23.27 and 50.30 % for the first fourprincipal components, respectively. Both the mean absolute errorand mean root mean squared error of the reconstructed time series by ISSAare also smaller than those by SSAM. The respective improvements are 34.45and 33.91 % when the missing data accounts for 60 %. The results fromreal incomplete time series also show that the standard deviation (SD)derived by ISSA is 12.27 mg L<sup>−1</sup>, smaller than the 13.48 mg L<sup>−1</sup> derivedby SSAM

Improved singular spectrum analysis for time series with missing data