?
Statistical Anomaly Detection Using LC-Curves: An Application to Geophysical Time Series
P. 384–392.
We adapt the Life Cycle (LC) curve methodology to the problem of statistical anomaly detection in time series. LC-curves transform a raw non-negative time series into a cumulative share profile that highlights when, within a fixed observation window, most of the signal intensity is concentrated. Deviations of this profile from the uniform-accumulation baseline serve as simple anomaly indicators that require no distributional assumptions. As an application, we apply the method to 5-min GPS displacement time series from the Nevada Geodetic Laboratory, comparing 196 earthquakes with M ≥ 6 against 104 seismically quiet intervals, and evaluate performance with Probability of Detection (POD), False Alarm Ratio (FAR), and Critical Success Index (CSI). The method achieves POD ≈ 0.82 (CSI = 0.62, FAR = 0.29) at the lowest tested threshold. Pre-event anomalies are statistically distinguishable from calm-period anomalies, demonstrating that LC-curves can e
Data Centers HVAC abnormal operating behaviour Detection with semi-supervised data-driven approaches
Manjurov A., Polyakov S., Ali S. et al., , in: Conference Proceedings: 2026 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT), 14-15 May 2026.: IEEE, 2026. P. 1–4.
The paper investigates semi-supervised anomaly detection in data center heating, ventilation, and air conditioning (HVAC) systems using multivariate time-series data under limited data availability. A one-year dataset with 10-minute resolution, including nine HVAC operational parameters and two exogenous variables (outdoor temperature and IT load), was analyzed across normal and several abnormal operating regimes. A rulebased threshold ...
Added: July 10, 2026
Butorova A., Bobakov V., Sergeev A. et al., European Physical Journal: Special Topics 2026 P. 1–19
This paper presents a review of artificial intelligence (AI) methods for failure prediction in data center cooling systems, with a focus on the integration of digital twins (DTs), physics-informed learning, and graph-based models. Positioned within complex network science, this review addresses a limitation of conventional graph approaches—their reliance on pairwise connectivity—whereas real-world failures often arise ...
Added: June 10, 2026
Ivanov S., Borisov V., Ali S. et al., , in: 2025 IEEE XVII International Scientific and Technical Conference on Actual Problems of Electronic Instrument Engineering (APEIE).: IEEE, 2025. Ch. 127 P. 1–7.
This paper investigates the problem of detecting slow refrigerant leaks in a data center cooling system using a graph neural network. The study addresses the challenge of early fault identification, proposing a method for constructing a topological graph based on the engineering diagram, the physical layout, and the cause-and-effect relationships in the cooling system. This ...
Added: December 19, 2025
Алескеров Ф. Т., Lola I. S., Asoskov D. et al., Вопросы экономики 2025 № 11 С. 143–157
The LC-curve method, a new approach to time series analysis, was applied to the composite Business Uncertainty Index (BUI), based on the results of regular Rosstat business surveys, which made it possible to analyze uncertainty trajectories across Russia's enlarged industries and sub-sectors using two index specifications: ex-ante (forecast) and ex-post (actual). The results of the ...
Added: October 13, 2025
Ivanov P., Shtark M., Kozhevnikov A. et al., IEEE Access 2025 Vol. 13 P. 25186–25197
Fault detection and diagnosis (FDD) is a critical challenge in industrial processes aimed at minimizing risks such as safety hazards, costly downtime, and suboptimal production. Traditional supervised FDD methods offer great performance while heavily relying on large volumes of labeled data, whereas unsupervised methods do not depend on labeled data, though are inferior in performance ...
Added: April 29, 2025
Sergeev A., Minchenkov V., Soldatov A. et al., ADVANCES IN ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING 2025 No. 1 P. 3344–3355
The automatic monitoring of manual assembly processes in production settings increasingly relies on advanced technologies, including computer vision models. These models are designed to detect and classify events such as the presence of components in an assembly area and the connection of these components. However, a significant challenge for detection and classification algorithms is their vulnerability ...
Added: April 2, 2025
V.P. Stepashkina, M.I. Hushchyn, Doklady Mathematics 2024 Vol. 110 No. 1 P. S95–S102
This paper presents the development and evaluation of methods for detecting cyberattacks on industrial systems using neural network approaches. The focus is on the task of detecting anomalies in multivariate time series, where the diversity and complexity of potential attack scenarios require the use of advanced models. To address these challenges, a transformer-based autoencoder architecture ...
Added: March 25, 2025
Sokolova A., Savchenko A., , in: 2023 IX International Conference on Information Technology and Nanotechnology (ITNT).: IEEE, 2023. P. 1–5.
In this paper, we explore the possibility to improve efficiency of face recognition using information about anomaly input images. Indeed, modern publicly-available datasets typically contain images of mostly middle-aged and Caucasian people, which cause most algorithms to fail on photos of older people or children, rarer ethnicities, poor-quality images, etc. Detection of such anomaly data ...
Added: June 13, 2023
Sokolova A., Savchenko A., , in: 2022 International Joint Conference on Neural Networks (IJCNN).: Institute of Electrical and Electronics Engineers Inc., 2022.
One of the main issues in face identification is to create a real-time application with high accuracy. Images are presented by high-dimensional feature vectors that are produced by convolutional neural networks. In order to effectively process such vectors, the hierarchical algorithm was proposed in this paper that applies sequential analysis to search the nearest neighbors ...
Added: May 29, 2023
Салин М. Б., Ponomarenko A., , in: Proceedings of Meetings on AcousticsVol. 44. Issue 1: 6th Underwater Acoustics Conference and Exhibition.: [б.и.], 2021.
This paper is devoted to methods of processing hydrophone records in order to identify specific signals, produced by marine mammals. The aim of processing is to detect a signal according to a certain pattern against the background of non-stationary noise. Development of a robust detection algorithm is of great interest because it could help to ...
Added: December 10, 2021
Ryzhikov A., Borisyak M., Ustyuzhanin A. et al., PeerJ Computer Science 2021 Vol. 7 Article e757
Anomaly detection is a challenging task that frequently arises in practically all areas of industry and science, from fraud detection and data quality monitoring to finding rare cases of diseases and searching for new physics. Most of the conventional approaches to anomaly detection, such as one-class SVM and Robust Auto-Encoder, are one-class classification methods, i.e., focus ...
Added: November 30, 2021
Pustokhina, I.V., Pustokhin D. A., Vaiyapuri T. et al., Safety Science 2021 Vol. 142 Article 105356
Anomaly detection in pedestrian walkways is an important research topic, commonly used to improve the safety of pedestrians. Due to the wide utilization of video surveillance systems and the increased quantity of captured videos, the traditional manual examination of labeling abnormal events is a tiresome task. So, an automated surveillance system that detects anomalies becomes ...
Added: September 20, 2021
Shalileh S., Shahdi S. O., , in: 2017 10th Iranian Conference on Machine Vision and Image Processing (MVIP).: IEEE, 2017. P. 31–38.
In recent years many research works have been devoted either to anomaly detection or anomaly classification. However, very few of them address both of them simultaneously. In this paper, we introduced a new method not only to detect and localize the abnormalities in crowded scenes but also to determine the class of abnormality. In This ...
Added: January 13, 2021
Bryksin T., Petukhov V., Alexin I. et al., , in: MSR '20: Proceedings of the 17th International Conference on Mining Software Repositories.: ACM Press, 2020. P. 455–465.
In this work, we apply anomaly detection to source code and byte-code to facilitate the development of a programming language and its compiler. We define anomaly as a code fragment that is different from typical code written in a particular programming language. Identifying such code fragments is beneficial to both language developers and end users, ...
Added: November 17, 2020
Maxim Borisyak, Artem Ryzhikov, Ustyuzhanin A. et al., Journal of Machine Learning Research 2020 Vol. 21 P. 1–22
Anomaly detection is not an easy problem since distribution of anomalous samples is unknown a priori. We explore a novel method that gives a trade-off possibility between one-class and two-class approaches, and leads to a better performance on anomaly detection problems with small or non-representative anomalous samples. The method is evaluated using several data sets ...
Added: March 13, 2020
Semenov A., Mazeev A., Dmitry D. et al., , in: Proceedings of the 4th GraphHPC conference on large-scale graph processing using HPC systemsVol. Vol-1981.: CEUR Workshop Proceedings, 2017. P. 1–9.
In the recent years, money laundering activity has become more and more ubiquitious all over the world. In the paper we survey the technical aspects of anti-money laundering systems (AML). We briefly present the principles of money laundering process and features of the illegal activity that can arise in the graph of money transactions, company ...
Added: October 30, 2019
Nazarov A., Виноградов Ю. В., Сычев А. К., Системы высокой доступности 2018 Т. 14 № 4 С. 20–22
The article studies the use of machine learning algorithms in solving information security problems, namely, in the construction of next-generation intrusion detection systems (IDS). The main drawbacks of traditional IDS (based on signature rules) are considered and methods for their solution are proposed using the algorithms of machine learning. The article presents new methods of ...
Added: February 26, 2019
Guimarães R. R., Passos L., Filho R. H. et al., IEEE Network 2019 Vol. 33 No. 2 P. 126–131
Distinguishing outliers from normal data in wireless sensor networks has been a big challenge in the anomaly detection domain, mostly due to the nature of the anomalies, such as software or hardware failures, reading errors or malicious attacks, just to name a few. In this article, we introduce an anomaly detection-based OPF classifier in the ...
Added: December 19, 2018
Azzolini V., Borisyak M., Cerminara G. et al., Journal of Physics: Conference Series 2018 Vol. 1085 No. 042015 P. 1–6
Daily operation of a large-scale experiment is a resource consuming task, particularly from perspectives of routine data quality monitoring. Typically, data comes from different sub-detectors and the global quality of data depends on the combinatorial performance of each of them. In this paper, the problem of identifying channels in which anomalies occurred is considered. We ...
Added: October 19, 2018
Ustyuzhanin A., Derkach D., Баранов А. С. et al., Journal of Physics: Conference Series 2017 Vol. 898 No. 9 P. 1–5
© Published under licence by IOP Publishing Ltd. Data quality monitoring, DQM, is crucial in a high-energy physics experiment to ensure the correct functioning of the experimental apparatus during the data taking. DQM at LHCb is carried out in two phases. The first one is performed on-site, in real time, using unprocessed data directly from ...
Added: February 25, 2018
Borisyak M., Ratnikov F., Derkach D. et al., Journal of Physics: Conference Series 2017 Vol. 898 No. 9 P. 1–6
Daily operation of a large-scale experiment is a challenging task, particularly from perspectives of routine monitoring of quality for data being taken. We describe an approach that uses Machine Learning for the automated system to monitor data quality, which is based on partial use of data qualified manually by detector experts. The system automatically classifies ...
Added: February 25, 2018
Artemov A., Burnaev E., Lokot A., , in: Proceedings of SPIE 9875, Eighth International Conference on Machine Vision (ICMV 2015).: Barcelona: SPIE, 2015. P. 987520–987525.
The paper is concerned with the sequential online change-point detection problem for a dynamical system driven by a quasiperiodic stochastic process. We propose a multicomponent time series model and an effective online decomposition algorithm to approximate the components of the models. Assuming the stationarity of the obtained components, we approach the change-point detection problem on ...
Added: January 26, 2018