Detecting Anomalies in Marine Data: A Framework for Time Series Analysis

被引：0

作者：

Del Buono, Nicoletta ^{[1
,2
]}

Esposito, Flavia ^{[1
,2
]}

Gargano, Grazia ^{[2
,3
]}

Selicato, Laura ^{[1
,2
]}

Taggio, Nicolo ^{[4
]}

Ceriola, Giulio ^{[4
]}

Iasillo, Daniela ^{[4
]}

机构：

[1] Univ Bari Aldo Moro, Dept Math, Via E Orabona 4, Bari, Italy

[2] INDAM GNCS Res Grp, Rome, Italy

[3] Ist Oncol Giovanni Paolo II, Bari, Italy

[4] Planetek Italia, Bari, Italy

来源：

MACHINE LEARNING, OPTIMIZATION, AND DATA SCIENCE, LOD 2022, PT I | 2023年 / 13810卷

关键词：

Anomaly; Outlier; Anomalies detection; Statistical models; Data pre-processing;

D O I：

10.1007/978-3-031-25599-1_36

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：485 / 500

页数：16

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