This paper proposes a novel methodology for recovering missing time series data, a crucial task for
subsequent Machine Learning (ML) analyses. The methodology is specifically applied to Significant
Wave Height (SWH) time series in the field of marine engineering. The proposed approach involves two
phases. Firstly, the SWH time series for each buoy is independently reconstructed using three transfer
function models: regression-based, correlation-based, and distance-based. The distance-based transfer
function exhibits the best overall performance. Secondly, Evolutionary Artificial Neural Networks
(EANNs) are utilised for the final recovery of each time series, using as inputs highly correlated buoys
that have been intermediately recovered. The EANNs are evolved considering two metrics, the novel
squared error relevance area, which balances the importance of extreme and around-mean values, and
the well-known mean squared error. The study considers SWH time series data from 15 buoys in two
coastal zones in the United States. The results demonstrate that the distance-based transfer function
is generally the best transfer function, and that EANNs outperform a range of state-of-the-art ML
techniques in 12 out of the 15 buoys, with a number of connections comparable to linear models.
Furthermore, the proposed methodology outperforms the two most popular approaches for time
series reconstruction, BRITS and SAITS, for all buoys except one. Therefore, the proposed methodology
provides a promising approach, which may be applied to time series from other fields, such as wind
or solar energy farms in the field of green energy
