This paper introduces a new algorithm for gap filling in univariate time series by using SSA. In this algorithm, the data before the missing values and the data after the missing values (in reverse order) are treated as two separate time series. Then using the recurrent SSA forecasting algorithm, two estimations of the missing values are obtained, one from the data before the missing values and one from the data after the missing values. Finally, using bootstrap resampling and a given weighting scheme, based on sample variances, these two estimates are combined to produce a unique estimation for missing values.
