In this talk, we introduce several algorithms using singular spectrum analysis for gap filling in univariate time series. In these algorithms, the data before missing values and the data after (in the reverse order) missing values treated as two separate time series. Then, using SSA forecasting, two estimations of the missing values will be provided. Finally, we combine these estimations and produce a unique estimation for missing values.
