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Воронеж: Истоки, 2020.
Neznanov A., Glushko A., Овчинников С. et al., В кн.: Интеллектуальный анализ данных в нефтегазовой отрасли.: М.: ООО «Геомодель Развитие», 2024. С. 140–143.
With the development of monitoring systems, now we have the opportunity to collect key performance indicators of devices in the process of artificial lift. Every day a huge amount of telemetry is generated by our devices, which can be used to forecast the working mode and health state of the equipment after the process of ...
Added: April 29, 2026
Ankudinov I., Социология: методология, методы, математическое моделирование 2025 № 61 С. 165–203
The changing political mood of Russians is a constant subject of interest for sociological agencies. With the development of the Internet, conventional questionnaire research began to be supplemented by online surveys and, despite some skepticism, by social media mining. This article attempts to adjust an accidental web-sample so as to bring its estimates closer to ...
Added: April 22, 2026
Hushchyn M., Arzymatov K., Derkach D., Machine Learning 2026 Vol. 115 Article 56
Moments when a time series changes its behavior are called change points. Occurrence of change point implies that the state of the system is altered and its timely detection might help to prevent unwanted consequences. In this paper, we present two change-point detection approaches based on neural networks and online learning. These algorithms demonstrate linear ...
Added: March 6, 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
Чертоганов К. А., Journal of Finance and Data Science 2025
This research aims to enhance the forecasting accuracy of extreme events, which pose significant challenges across various domains such as meteorology, finance, and public health. The study investigates the integration of cross-correlation and partial autocorrelation functions (PACF) with machine learning techniques to address the limitations of traditional forecasting methods and improve predictive reliability and interpretability. ...
Added: April 29, 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
Makeeva N., Прикладная эконометрика 2025 Т. 79 С. 27–49
The paper presents the results of an accuracy analysis of nowcasting models for Russia’s GDP and its components based on usage data for the period from the first quarter of 2014 to the third quarter of 2023. The novelty of the study lies in comparing the accuracy of various models — MIDAS, MFBVAR, DFM models, ...
Added: April 19, 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
Shvedov A. S., Sviyazov V., В кн.: Системное моделирование социально-экономических процессов: труды 46-ой международной научной школы-семинара, г. Уфа, 9 - 15 октября 2023 г.: Воронеж: Истоки, 2024. С. 526–531.
The generalized autoregressive conditional heteroscedasticity model is widely applied to financial time series. There are further generalizations of this model. One of such generalizations is a combination of Takagi–Sugeno type fuzzy systems and autoregressive conditional heteroscedasticity models. The Takagi–Sugeno fuzzy systems advantage is that there is a standalone generalized autoregressive conditional heteroscedasticity model constructed for ...
Added: June 26, 2024
Egorova L., В кн.: XIV Всероссийское совещание по проблемам управления ВСПУ-2024, 17-20 июня 2024 г., Москва.: [б.и.], 2024. С. 496–500.
Added: June 19, 2024
Zhevnenko D., Kazantsev M., Makarov I., Journal of Industrial Information Integration 2023 Vol. 33 Article 100444
The paper deals with the problem of controlling the state of industrial devices according to the readings of their sensors. The current methods are based on an approach to feature extraction in which the prediction occurs. We propose an interaction method of multiple blocks of different complexity, which aggregate information differently over time, to create ...
Added: February 15, 2024
Sizykh D., Sizykh N., Чебоксары: ИД «Среда», 2023.
The monograph presents research materials based on the results of assessment and analysis of the dynamic characteristics of time series of stock quotes. Optimal estimates were selected and justified, the applying of which makes possible to improve the quality and efficiency of the forecasting stock quotes, building and rebalancing of investment portfolios, risk management, etc. ...
Added: December 22, 2023
Sviyazov V., Control Sciences 2022 No. 6 P. 21–28
Volatility modeling and forecasting is a topical problem both in scientific circles and in the practice. This paper develops an approach combining the GARCH model and fuzzy logic. The Takagi–Sugeno fuzzy inference scheme is adopted to fuzzify an original autoregression model (the conditional heteroskedasticity model). As a result, several different local GARCH models can be ...
Added: December 6, 2023