Automated data-driven approach for gap filling in the time series using evolutionary learning
This work addresses a domain-specific problem for data scientists and analysts in fields like environmental and economic monitoring, but it is incremental as it builds on existing data-driven and evolutionary methods.
The paper tackles the problem of filling gaps in time series data by proposing an automated evolutionary learning approach to identify optimal model structures, achieving higher quality gap restoration and improved forecasting effectiveness as confirmed in experiments on synthetic and real datasets.
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.