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

arXiv: https://arxiv.org/abs/2103.01124 · HTML: https://mlsanity.com/paper/2103.01124/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 29 references that are in the map.
- First posted on arXiv 1 Mar 2021.

## Builds on (most cited first, 3 of 3)

- [Recurrent Neural Networks for Multivariate Time Series with Missing Values](https://mlsanity.com/paper/1606.01865/) (2016)
- [GP-VAE: Deep Probabilistic Time Series Imputation](https://mlsanity.com/paper/1907.04155/) (2019)
- [Bidirectional Recurrent Neural Networks as Generative Models - Reconstructing Gaps in Time Series](https://mlsanity.com/paper/1504.01575/) (2015)

## Abstract (arXiv)

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.