Gap-filling by the empirical mode decomposition

We propose a novel gap-filling technique, based on the empirical mode decomposition (EMD). The idea is that a signal with missing data can be decomposed into a set of intrinsic mode functions (IMFs) with missing data. Filling the gaps in each IMF should be easier than filling the gaps in the original signal. This is because each IMF varies much more slowly than the original signal, and also because the IMFs are known to have useful regularity properties. We demonstrate the performance of our technique on environmental pollutant data.