UMAD-G: Unsupervised Multi-Modal Time Series Anomaly Detection via Graph

Jinhyeok Lee, Jungeol Baek

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In the wake of Industry 4.0, many companies are equipping their systems with more sensors and collecting huge amounts of sensor data. This data, in the form of time series, plays an important role in increasing productivity and implementing smart factories. One of the most important things is to detect anomalies caused by various factors (equipment ageing, sudden stops in the production line, interference from external factors). One of the limitations of existing time series anomaly detection research is that it does not take into account the complex spatial dependencies (inter-And intra-modal correlations) of sensor data. Therefore, we propose a model that can accurately and effectively detect outliers in multi-modal time series collected from manufacturing facilities by utilizing Graph Attention Networks. Our proposed model demonstrates highest performance through performance comparison with six baselines.

Original languageEnglish
Title of host publication6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages653-656
Number of pages4
ISBN (Electronic)9798350344349
DOIs
Publication statusPublished - 2024
Event6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024 - Osaka, Japan
Duration: 2024 Feb 192024 Feb 22

Publication series

Name6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

Conference

Conference6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024
Country/TerritoryJapan
CityOsaka
Period24/2/1924/2/22

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

Keywords

  • anomaly detection
  • graph attention networks
  • multi-modal time series
  • sensor
  • unsupervised learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Information Systems
  • Safety, Risk, Reliability and Quality
  • Health Informatics

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