  We investigate the use of matrix completion methods for time-series
imputation. Specifically we consider low-rank completion of the block-Hankel
matrix representation of a time-series. Simulation experiments are used to
compare the method with five recognised imputation techniques with varying
levels of computational effort. The Hankel Imputation (HI) method is seen to
perform competitively at interpolating missing time-series data, and shows
particular potential for reproducing sharp peaks in the data.
