An ensemble framework for the analysis of time series from marine backgrounds is proposed to finally identify and classify anomalies in data time series collected from European Union's Earth Observation Programme Copernicus and Marine-EO project. The framework aims to estimate a prediction model for anomalies detection when new records are explored and then rank the magnitude of the anomalies eventually detected in some biogeochemical parameters of marine and ocean waters, such as chlorophyll-a concentrations, surface temperature profiles and dissolved oxygen.
Detecting Anomalies in Marine Data: A Framework for Time Series Analysis
Del Buono, N;Esposito, FMembro del Collaboration Group
;Gargano, G;Selicato, L;
2023-01-01
Abstract
An ensemble framework for the analysis of time series from marine backgrounds is proposed to finally identify and classify anomalies in data time series collected from European Union's Earth Observation Programme Copernicus and Marine-EO project. The framework aims to estimate a prediction model for anomalies detection when new records are explored and then rank the magnitude of the anomalies eventually detected in some biogeochemical parameters of marine and ocean waters, such as chlorophyll-a concentrations, surface temperature profiles and dissolved oxygen.File in questo prodotto:
Non ci sono file associati a questo prodotto.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
