Skip to main content
Erschienen in: Journal of Intelligent Manufacturing 6/2021

18.05.2020

Bound smoothing based time series anomaly detection using multiple similarity measures

verfasst von: Wenqing Wang, Junpeng Bao, Tao Li

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 6/2021

Einloggen

Aktivieren Sie unsere intelligente Suche um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Time series data is pervasive in many applications and the anomaly detection about it is important, which will provide the early warning of some unexpected patterns. In this paper, we propose a multiple similarity based anomalous subsequences detection method, which is unsupervised and domain knowledge free. Firstly, to improve the time efficiency, an anomaly candidates selection scheme is introduced based on the locality sensitive hashing (LSH), which considers a subsequence that does not collide with the others as a potential anomaly. However, if the raw time series is noisy and the anomaly is subtle, the performance of LSH will be degraded. In order to address this problem, we present a smoothing method to remove the noise and highlight the anomalous part in a time series, which can help to decrease the collision probability between an anomaly and the other subsequences. Secondly, we employ Pareto analysis to incorporate multiple similarity measures since there are different types of anomalies in real applications. It is unlikely that a single similarity measure can perform consistently well on different types of anomalies. Thirdly a new anomaly score scheme is provided to evaluate each anomaly candidate, which is based on the number of non-dominated vectors. Finally, we conduct extensive experiments on benchmark datasets from diverse domains and compare our method with the state-of-the-art approaches. The results show that our method can reach higher accuracy.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Anhänge
Nur mit Berechtigung zugänglich
Literatur
Zurück zum Zitat Cheng, H., Tan, P. N., Potter, C., & Klooster., S. (2008). A robust graph-based algorithm for detection and characterization of anomalies in noisy multivariate time series. In 2008 IEEE international conference on data mining workshops (pp. 349–358). IEEE. https://doi.org/10.1109/ICDMW.2008.48. Cheng, H., Tan, P. N., Potter, C., & Klooster., S. (2008). A robust graph-based algorithm for detection and characterization of anomalies in noisy multivariate time series. In 2008 IEEE international conference on data mining workshops (pp. 349–358). IEEE. https://​doi.​org/​10.​1109/​ICDMW.​2008.​48.
Zurück zum Zitat Malhotra, P., Lovekesh, V., Shroff, G., & Agarwal, P. (2015). Long short term memory networks for anomaly detection in time series. In 2015 European symposium on artificial neural networks, computional intelligence and machine learning (pp. 89–94). http://www.i6doc.com/en/. Malhotra, P., Lovekesh, V., Shroff, G., & Agarwal, P. (2015). Long short term memory networks for anomaly detection in time series. In 2015 European symposium on artificial neural networks, computional intelligence and machine learning (pp. 89–94). http://​www.​i6doc.​com/​en/​.
Zurück zum Zitat Nascimento, E. G. S., Tavares, O. D. L., & Souza, A. F. D. (2015). A cluster-based algorithm for anomaly detection in time series using Mahalanobis distance. In Proceedings of the international conference on artificial intelligence (pp. 622–628). Nascimento, E. G. S., Tavares, O. D. L., & Souza, A. F. D. (2015). A cluster-based algorithm for anomaly detection in time series using Mahalanobis distance. In Proceedings of the international conference on artificial intelligence (pp. 622–628).
Metadaten
Titel
Bound smoothing based time series anomaly detection using multiple similarity measures
verfasst von
Wenqing Wang
Junpeng Bao
Tao Li
Publikationsdatum
18.05.2020
Verlag
Springer US
Erschienen in
Journal of Intelligent Manufacturing / Ausgabe 6/2021
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI
https://doi.org/10.1007/s10845-020-01583-0

Weitere Artikel der Ausgabe 6/2021

Journal of Intelligent Manufacturing 6/2021 Zur Ausgabe

    Marktübersichten

    Die im Laufe eines Jahres in der „adhäsion“ veröffentlichten Marktübersichten helfen Anwendern verschiedenster Branchen, sich einen gezielten Überblick über Lieferantenangebote zu verschaffen.