Deep Learning for Time Series Anomaly Detection: A Survey

• URL: http://arxiv.org/abs/2211.05244v3
• Date: Tue, 28 May 2024 04:36:22 GMT
• Title: Deep Learning for Time Series Anomaly Detection: A Survey
• Authors: Zahra Zamanzadeh Darban, Geoffrey I. Webb, Shirui Pan, Charu C. Aggarwal, Mahsa Salehi,
• Abstract summary: Time series anomaly detection has applications in a wide range of research fields and applications, including manufacturing and healthcare. The large size and complex patterns of time series have led researchers to develop specialised deep learning models for detecting anomalous patterns. This survey focuses on providing structured and comprehensive state-of-the-art time series anomaly detection models through the use of deep learning.
• Score: 53.83593870825628
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Time series anomaly detection has applications in a wide range of research fields and applications, including manufacturing and healthcare. The presence of anomalies can indicate novel or unexpected events, such as production faults, system defects, or heart fluttering, and is therefore of particular interest. The large size and complex patterns of time series have led researchers to develop specialised deep learning models for detecting anomalous patterns. This survey focuses on providing structured and comprehensive state-of-the-art time series anomaly detection models through the use of deep learning. It providing a taxonomy based on the factors that divide anomaly detection models into different categories. Aside from describing the basic anomaly detection technique for each category, the advantages and limitations are also discussed. Furthermore, this study includes examples of deep anomaly detection in time series across various application domains in recent years. It finally summarises open issues in research and challenges faced while adopting deep anomaly detection models.

Related papers

• Open Challenges in Time Series Anomaly Detection: An Industry Perspective [0.0]
  We list several areas that are of practical relevance and that we believe are either under-investigated or missing entirely from the current discourse. Based on an investigation of systems deployed in a cloud environment, we motivate the areas of streaming algorithms, human-in-the-loop scenarios, point processes, conditional anomalies and populations analysis of time series.
  arXiv  Detail & Related papers  (2025-02-08T00:38:07Z)
• Dive into Time-Series Anomaly Detection: A Decade Review [19.883791946730494]
  Time-series anomaly detection has been an important activity, entailing various applications in fields such as cyber security, financial markets, law enforcement, and health care. This survey groups and summarizes anomaly detection existing solutions under a process-centric taxonomy in the time series context. In addition to giving an original categorization of anomaly detection methods, we also perform a meta-analysis of the literature and outline general trends in time-series anomaly detection research.
  arXiv  Detail & Related papers  (2024-12-29T16:11:46Z)
• Open-Vocabulary Video Anomaly Detection [57.552523669351636]
  Video anomaly detection (VAD) with weak supervision has achieved remarkable performance in utilizing video-level labels to discriminate whether a video frame is normal or abnormal. Recent studies attempt to tackle a more realistic setting, open-set VAD, which aims to detect unseen anomalies given seen anomalies and normal videos. This paper takes a step further and explores open-vocabulary video anomaly detection (OVVAD), in which we aim to leverage pre-trained large models to detect and categorize seen and unseen anomalies.
  arXiv  Detail & Related papers  (2023-11-13T02:54:17Z)
• Prototypes as Explanation for Time Series Anomaly Detection [6.051581987453758]
  This paper proposes ProtoAD, using prototypes as the example-based explanation for the state of regular patterns during anomaly detection. By visualizing both the latent space and input space prototypes, we intuitively demonstrate how regular data are modeled and why specific patterns are considered abnormal.
  arXiv  Detail & Related papers  (2023-07-04T09:40:30Z)
• Precursor-of-Anomaly Detection for Irregular Time Series [31.73234935455713]
  We present a novel type of anomaly detection, called Precursor-of-Anomaly (PoA) detection. To solve both problems at the same time, we present a neural controlled differential equation-based neural network and its multi-task learning algorithm.
  arXiv  Detail & Related papers  (2023-06-27T14:10:09Z)
• Causality-Based Multivariate Time Series Anomaly Detection [63.799474860969156]
  We formulate the anomaly detection problem from a causal perspective and view anomalies as instances that do not follow the regular causal mechanism to generate the multivariate data. We then propose a causality-based anomaly detection approach, which first learns the causal structure from data and then infers whether an instance is an anomaly relative to the local causal mechanism. We evaluate our approach with both simulated and public datasets as well as a case study on real-world AIOps applications.
  arXiv  Detail & Related papers  (2022-06-30T06:00:13Z)
• Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection [90.32910087103744]
  A few labeled anomaly examples are often available in many real-world applications. These anomaly examples provide valuable knowledge about the application-specific abnormality. Those anomalies seen during training often do not illustrate every possible class of anomaly. This paper tackles open-set supervised anomaly detection.
  arXiv  Detail & Related papers  (2022-03-28T05:21:37Z)
• Consistency of mechanistic causal discovery in continuous-time using Neural ODEs [85.7910042199734]
  We consider causal discovery in continuous-time for the study of dynamical systems. We propose a causal discovery algorithm based on penalized Neural ODEs.
  arXiv  Detail & Related papers  (2021-05-06T08:48:02Z)
• TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks [73.01104041298031]
  TadGAN is an unsupervised anomaly detection approach built on Generative Adversarial Networks (GANs) To capture the temporal correlations of time series, we use LSTM Recurrent Neural Networks as base models for Generators and Critics. To demonstrate the performance and generalizability of our approach, we test several anomaly scoring techniques and report the best-suited one.
  arXiv  Detail & Related papers  (2020-09-16T15:52:04Z)
• Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Art [0.0]
  Anomaly detection for time-series data has been an important research field for a long time. Recent years an increasing number of machine learning algorithms have been developed to detect anomalies on time-series. Researchers tried to improve these techniques using (deep) neural networks.
  arXiv  Detail & Related papers  (2020-04-01T13:22:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.