Anomaly detection in multivariate temporal data for vessels abnormal behaviour detection

2019

The growing number of deployed data mining systems leverage the interest in temporal data anomaly detection. From cyber-security or finance to heart-diseases detection, unexpected data often incorporate critical information that must be analysed. Data anomalies have long been studied from an univariate perspective where only one data dimension changes over time. Few works have been dedicated to multivariate anomaly detection. In this work we provide a comprehensive and structured analysis of the main definitions, state-of-art methods and approaches focusing multivariate temporal data anomaly detection. Our research focus on dealing with variable length data series with millions of samples and multiple feature categories, either static or dynamic, real or categorical valued. We describe a case-study in the maritime domain investigating the unusual spatio-temporal behaviour of commercial vessels and experiment over two open datasets and one got from the MARISA H2020 Project1.

Authors in the community:

• 

  R

  Rui Filipe Pedroso Maia

  ist142764

• 

  C

  Claudia Martins Antunes

  ist14046

  Affiliations and sources

  IST > DEI

  Departamento de Engenharia Informática

  In: scopus

  ID: 2-s2.0-85086631407

https://aisel.aisnet.org/capsi2019/31/

Atas da Conferencia da Associacao Portuguesa de Sistemas de Informacao

Lisbon, Portugal

10/11/2019

10/12/2019

computer-and-information-sciences - Computer and information sciences

eng - English