Filling Gaps in Micro-Meteorological Data
Abstract
Filling large data-gaps in Micro-Meteorological data has mostly been done using interpolation techniques based on a marginal distribution sampling. Those methods work well but need a large horizon of the previous events to achieve good results since they do not model the system but only rely on previously encountered iterations. In this paper, we propose to use multi-head deep attention networks to fill gaps in Micro-Meteorological Data. This methodology couples large-scale information extraction with modeling capabilities that cannot be achieved by interpolation-like techniques. Unlike Bidirectional RNNs, our architecture is not recurrent, it is simple to tune and our data efficiency is higher. We apply our architecture to real-life data and clearly show its applicability in agriculture, furthermore, we show that it could be used to solve related problems such as filling gaps in cyclic-multivariate-time-series.
Cite
Text
Richard et al. "Filling Gaps in Micro-Meteorological Data." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2020. doi:10.1007/978-3-030-67670-4_7Markdown
[Richard et al. "Filling Gaps in Micro-Meteorological Data." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2020.](https://mlanthology.org/ecmlpkdd/2020/richard2020ecmlpkdd-filling/) doi:10.1007/978-3-030-67670-4_7BibTeX
@inproceedings{richard2020ecmlpkdd-filling,
title = {{Filling Gaps in Micro-Meteorological Data}},
author = {Richard, Antoine and Fine, Lior and Rozenstein, Offer and Tanny, Josef and Geist, Matthieu and Pradalier, Cédric},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2020},
pages = {101-117},
doi = {10.1007/978-3-030-67670-4_7},
url = {https://mlanthology.org/ecmlpkdd/2020/richard2020ecmlpkdd-filling/}
}