  Signal data often contains missing values. Effective replacement (imputation)
of the missing values can have significant positive effects on processing the
signal. In this paper, we compare three commonly employed methods for
estimating missing values in time series data: forward fill, backward fill, and
mean fill. We carry out a large scale experimental analysis using 3,600
AR(1)-based simulated time series to determine the optimal method for
estimating missing values. The results of the numerical experiments show that
the forward and backward fill methods are better suited for times series with
large positive correlations, while the mean fill method is better suited for
times series with low or negative correlations. The extensive and exhaustive
nature of the numerical experiments provides a definitive answer to the
comparison of the three imputation methods.
