2022 • Conference Paper
Gap filling in air temperature series by matrix completion methods
Authors:
Loucheur, Benoît ,
Absil, Pierre-Antoine ,
Journée, Michel
Published in:
ESANN 2022, European Symposium on Artificial Neural Networks
Quality control of meteorological data is an important part of atmospheric analysis and prediction, as missing or erroneous data can have a negative impact on the accuracy of these environmental products. In practice, the presence of missing data in the weather series is quite common and problematic for many uses. We have compared the performance of matrix completion methods with the state of the art to solve this missing data problem. The experimental results were carried out using the daily minimum and maximum temperature measurements of the network of weather stations operated by the Royal Meteorological Institute (RMI) of Belgium.