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A Hybrid Deep Learning Framework for Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data

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Date
2020
Author
Karadayı, Yıldız
Aydın, Mehmet Nafiz
Öǧrenci, Arif Selçuk
Abstract
Multivariate time-series data with a contextual spatial attribute have extensive use for finding anomalous patterns in a wide variety of application domains such as earth science, hurricane tracking, fraud, and disease outbreak detection. In most settings, spatial context is often expressed in terms of ZIP code or region coordinates such as latitude and longitude. However, traditional anomaly detection techniques cannot handle more than one contextual attribute in a unified way. In this paper, a new hybrid approach based on deep learning is proposed to solve the anomaly detection problem in multivariate spatio-temporal dataset. It works under the assumption that no prior knowledge about the dataset and anomalies are available. The architecture of the proposed hybrid framework is based on an autoencoder scheme, and it is more efficient in extracting features from the spatio-temporal multivariate datasets compared to the traditional spatio-temporal anomaly detection techniques. We conducted extensive experiments using buoy data of 2005 from National Data Buoy Center and Hurricane Katrina as ground truth. Experiments demonstrate that the proposed model achieves more than 10% improvement in accuracy over the methods used in the comparison where our model jointly processes the spatial and temporal dimensions of the contextual data to extract features for anomaly detection.

Source

Applıed Scıences-Basel

Issue

15

Volume

10

URI

https://doi.org/10.3390/app10155191
https://hdl.handle.net/20.500.12469/3498

Collections

  • Araştırma Çıktıları / Scopus [1565]
  • Araştırma Çıktıları / WOS [1518]
  • Doktora Tezleri [86]
  • Elektrik-Elektronik Mühendisliği / Electrical - Electronics Engineering [321]
  • Yönetim Bilişim Sistemleri / Management Information Systems [145]

Keywords

Spatio-temporal anomaly detection
Unsupervised learning
Multivariate data
Deep learning
CNN
LSTM
Hurricane tracking
Hurricane Katrina

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DSpace software copyright © 2002-2015  DuraSpace
Contact Us | Send Feedback
Theme by 
@mire NV