Precursor-of-Anomaly Detection for Irregular Time Series
- URL: http://arxiv.org/abs/2306.15489v3
- Date: Fri, 13 Oct 2023 06:36:20 GMT
- Title: Precursor-of-Anomaly Detection for Irregular Time Series
- Authors: Sheo Yon Jhin, Jaehoon Lee, Noseong Park
- Abstract summary: 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.
- Score: 31.73234935455713
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Anomaly detection is an important field that aims to identify unexpected
patterns or data points, and it is closely related to many real-world problems,
particularly to applications in finance, manufacturing, cyber security, and so
on. While anomaly detection has been studied extensively in various fields,
detecting future anomalies before they occur remains an unexplored territory.
In this paper, we present a novel type of anomaly detection, called
Precursor-of-Anomaly (PoA) detection. Unlike conventional anomaly detection,
which focuses on determining whether a given time series observation is an
anomaly or not, PoA detection aims to detect future anomalies before they
happen. To solve both problems at the same time, we present a neural controlled
differential equation-based neural network and its multi-task learning
algorithm. We conduct experiments using 17 baselines and 3 datasets, including
regular and irregular time series, and demonstrate that our presented method
outperforms the baselines in almost all cases. Our ablation studies also
indicate that the multitasking training method significantly enhances the
overall performance for both anomaly and PoA detection.
Related papers
- Graph Spatiotemporal Process for Multivariate Time Series Anomaly
Detection with Missing Values [67.76168547245237]
We introduce a novel framework called GST-Pro, which utilizes a graphtemporal process and anomaly scorer to detect anomalies.
Our experimental results show that the GST-Pro method can effectively detect anomalies in time series data and outperforms state-of-the-art methods.
arXiv Detail & Related papers (2024-01-11T10:10:16Z) - CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly Detection [53.83593870825628]
One main challenge in time series anomaly detection (TSAD) is the lack of labelled data in many real-life scenarios.
Most of the existing anomaly detection methods focus on learning the normal behaviour of unlabelled time series in an unsupervised manner.
We introduce a novel end-to-end self-supervised ContrAstive Representation Learning approach for time series anomaly detection.
arXiv Detail & Related papers (2023-08-18T04:45:56Z) - AGAD: Adversarial Generative Anomaly Detection [12.68966318231776]
Anomaly detection suffered from the lack of anomalies due to the diversity of abnormalities and the difficulties of obtaining large-scale anomaly data.
We propose Adversarial Generative Anomaly Detection (AGAD), a self-contrast-based anomaly detection paradigm.
Our method generates pseudo-anomaly data for both supervised and semi-supervised anomaly detection scenarios.
arXiv Detail & Related papers (2023-04-09T10:40:02Z) - Towards Meaningful Anomaly Detection: The Effect of Counterfactual
Explanations on the Investigation of Anomalies in Multivariate Time Series [0.0]
Among the anomalies detected may be events that are rare, e.g., a planned shutdown of a machine, but are not the actual event of interest.
We propose to support this anomaly investigation by providing explanations of anomaly detection.
We conduct a behavioral experiment using records of taxi rides in New York City as a testbed.
arXiv Detail & Related papers (2023-02-07T07:27:26Z) - Are we certain it's anomalous? [57.729669157989235]
Anomaly detection in time series is a complex task since anomalies are rare due to highly non-linear temporal correlations.
Here we propose the novel use of Hyperbolic uncertainty for Anomaly Detection (HypAD)
HypAD learns self-supervisedly to reconstruct the input signal.
arXiv Detail & Related papers (2022-11-16T21:31:39Z) - Deep Learning for Time Series Anomaly Detection: A Survey [53.83593870825628]
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.
arXiv Detail & Related papers (2022-11-09T22:40:22Z) - 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) - 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.