Aim of this paper is to propose the use of a Generative Adversarial Imputation Nets (GAIN) to evaluate the quality of time series of environmental data. These time series are becoming more and more popular because they are used by many services available online such as weather forecasting, weather alert systems, etc. The performance of these systems is strictly correlated to the quality of their input data. For this reason, the quality of meteorological time series is becoming a key point for many services.The GAIN method is used to impute missing data in meteorological time series derived by two different kinds of sensors (terrestrial weather station and virtual sensor). The quality of the rebuilt series can be used as a measure of the quality of the original one.
A proposal of a new technique for meteorological data quality evaluation / Popolizio, M; Amato, A; Di Lecce, V. - (2021), pp. -6. [10.1109/CIVEMSA52099.2021.9493673]
A proposal of a new technique for meteorological data quality evaluation
Popolizio, M;Di Lecce, V
2021-01-01
Abstract
Aim of this paper is to propose the use of a Generative Adversarial Imputation Nets (GAIN) to evaluate the quality of time series of environmental data. These time series are becoming more and more popular because they are used by many services available online such as weather forecasting, weather alert systems, etc. The performance of these systems is strictly correlated to the quality of their input data. For this reason, the quality of meteorological time series is becoming a key point for many services.The GAIN method is used to impute missing data in meteorological time series derived by two different kinds of sensors (terrestrial weather station and virtual sensor). The quality of the rebuilt series can be used as a measure of the quality of the original one.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
