Dealing with time series with missing values, including those afflicted by
low quality or over-saturation, presents a significant signal processing
challenge. The task of recovering these missing values, known as imputation,
has led to the development of several algorithms. However, we have observed
that the efficacy of these algorithms tends to diminish when the time series
exhibit non-stationary oscillatory behavior. In this paper, we introduce a
novel algorithm, coined Harmonic Level Interpolation (HaLI), which enhances the
performance of existing imputation algorithms for oscillatory time series.
After running any chosen imputation algorithm, HaLI leverages the harmonic
decomposition based on the adaptive nonharmonic model of the initial imputation
to improve the imputation accuracy for oscillatory time series. Experimental
assessments conducted on synthetic and real signals consistently highlight that
HaLI enhances the performance of existing imputation algorithms. The algorithm
is made publicly available as a readily employable Matlab code for other
researchers to use