Deep learning for anomaly detection in multivariate time series: Approaches, applications, and challenges
- Authors
- Li, G.; Jung, Jason J.
- Issue Date
- Mar-2023
- Publisher
- Elsevier B.V.
- Keywords
- Anomaly detection; Multivariate time series; Research challenge
- Citation
- Information Fusion, v.91, pp 93 - 102
- Pages
- 10
- Journal Title
- Information Fusion
- Volume
- 91
- Start Page
- 93
- End Page
- 102
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/59190
- DOI
- 10.1016/j.inffus.2022.10.008
- ISSN
- 1566-2535
1872-6305
- Abstract
- Anomaly detection has recently been applied to various areas, and several techniques based on deep learning have been proposed for the analysis of multivariate time series. In this study, we classify the anomalies into three types, namely abnormal time points, time intervals, and time series, and review the state-of-the-art deep learning techniques for the detection of each of these types. Long short-term memory and autoencoders are the most commonly used methods for detecting abnormal time points and time intervals. In addition, some studies have implemented dynamic graphs to examine relational features between the time series and detect abnormal time intervals. However, anomaly detection still faces some limitations and challenges, such as the explainability of anomalies. Many studies have focused only on anomaly detection methods but failed to consider the reasons for the anomalies. Therefore, increasing the explainability of anomalies is an important research topic in anomaly detection. © 2022 Elsevier B.V.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - College of Software > School of Computer Science and Engineering > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.