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Subject: Time series analysis · Anomaly detection · Variational au- toencoder · Mahalanobis distance.

Year: 2020

Type: Article

Title: Time series anomaly detection with Variational Autoencoder using Mahalanobis distance

Author: Gjorgjiev, L.
Author: Gievska, Sonja

Abstract: Two themes have dominated the research on anomaly de- tection in time series data, one related to explorations of deep architec- tures for the task, and the other, equally important, the creation of large benchmark datasets. In line with the current trends, we have proposed several deep learning architectures based on Variational Autoencoders that have been evaluated for detecting cyber-attacks on water distribution system on the BATADAL challenge task and dataset. The second research aim of this study was to examine the impact of using Mahalanobis distance as a reconstruction error on the performance of the proposed models.

Publisher: Springer

Relation: ICT Innovations. Machine Learning and Applications

Identifier: oai:repository.ukim.mk:20.500.12188/8892
Identifier: http://hdl.handle.net/20.500.12188/8892

Title	Date	Views
Time series anomaly detection with Variational Autoencoder using Mahalanobis distance	2020	18