  Time series analysis is widely used in various fields of science and
industry. However, the vast majority of the time series obtained from real
sources contain a large number of gaps, have a complex character, and can
contain incorrect or missed parts. So, it is useful to have a convenient,
efficient, and flexible instrument to fill the gaps in the time series. In this
paper, we propose an approach for filling the gaps by the evolutionary
automatic machine learning, that is implemented as a part of the FEDOT
framework. Automated identification of the optimal data-driven model structure
allows the adopting of the gap filling strategy to the specific problem. As a
case study, the multivariate sea surface height dataset is used. During the
experimental studies, the proposed approach was compared with other gap-filling
methods and the composite models allow obtaining the higher quality of the gap
restoration.
