Introducing Gradient Boosting as a universal gap filling tool for meteorological time series

Introducing Gradient Boosting as a universal gap filling tool for meteorological time series

In this article, Gradient Boosting (gb) is introduced as an easily adaptable machine learning method to fill gaps caused by missing or erroneous data in meteorological time series. The gb routine is applied on a large data set of hourly time series of the measurands: air temperature, wind speed and...

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Bibliographic Details
Main Authors: Philipp Körner, Rico Kronenberg, Sandra Genzel, Christian Bernhofer
Format: Article
Language:English
Published: Borntraeger 2018-12-01
Series:Meteorologische Zeitschrift
Subjects:
gap filling
missing values
hourly resolution
gradient boosting
xgboost
air temperature
wind speed
relative humidity
Germany
xgb
imputation
Online Access:http://dx.doi.org/10.1127/metz/2018/0908

Internet

http://dx.doi.org/10.1127/metz/2018/0908

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