Automated data-driven approach for gap filling in the time series using evolutionary learning

TL;DR

This paper introduces an automated evolutionary learning method for filling gaps in time series data by identifying optimal data-driven models, enhancing gap restoration quality and forecasting effectiveness without human intervention.

Contribution

It presents a novel adaptive, automated approach for time series gap filling using evolutionary learning to identify optimal model structures.

Findings

Higher quality gap restoration achieved

Improved forecasting effectiveness demonstrated

Applicable to synthetic and real datasets

Abstract

In the paper, we propose an adaptive data-driven model-based approach for filling the gaps in time series. The approach is based on the automated evolutionary identification of the optimal structure for a composite data-driven model. It allows adapting the model for the effective gap-filling in a specific dataset without the involvement of the data scientist. As a case study, both synthetic and real datasets from different fields (environmental, economic, etc) are used. The experiments confirm that the proposed approach allows achieving the higher quality of the gap restoration and improve the effectiveness of forecasting models.