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О МЕТОДАХ ОЦЕНКИ МЕРЫ СОВМЕСТНОЙ МОНОТОННОСТИ ВРЕМЕННЫХ РЯДОВ
С. 328–335.
Bulychev A., Зайцев Р. Д.
The paper discusses the special properties of two empirical correlation coefficients, reflecting the synchronicity (joint monotonicity) the dynamics of two time series. The obtained estimates can be used to study the properties of random processes and in machine learning problems. With the help of numerical modeling, the result is obtained: both correlation coefficients allow to identify differences in the joint monotony, while one of them also allows to identify differences in the trends of time series.
In book
Ростов н/Д: Ростовский государственный экономический университет "РИНХ", 2018.
Nigmatullin R. R., Dorokhin S. V., Ivchenko A., Mathematics 2021 Vol. 9 No. 4 Article 381
n this paper, we focus on the generalization of the Hurst empirical law and suggest a set of reduced parameters for quantitative description of long-time series. These series are usually considered as a specific response of a complex system (economic, geophysical, electromagnetic and other systems), where successive fixations of external factors become impossible. We consider ...
Added: June 27, 2026
Karpukhin I., Savchenko A., , in: Proceedings of the AAAI Conference on Artificial Intelligence. AAAI-26: AAAI Technical Track on Planning, Routing, and Scheduling; AAAI Technical Track on Reasoning under Uncertainty; AAAI Technical Track on Search and Optimization. Main Track, volume 40 no. 43.: American Association for Artificial Intelligence (AAAI) Press, 2026. P. 22536–22544.
Long-horizon events forecasting is a crucial task across various domains, including retail, finance, healthcare, and social networks. Traditional models for event sequences often extend to forecasting on a horizon using an autoregressive (recursive) multi-step strategy, which has limited effectiveness due to typical convergence to constant or repetitive outputs. To address this limitation, we introduce DEF, a novel approach for simultaneous forecasting of ...
Added: June 17, 2026
Karacharovskiy V., Ларина У. С., Резмерица А. А., Социологические исследования 2026 № 7 С. 60–75
Based on the neural network approach, the decomposition of the index of social mood in Russia for the period 2004-2024 was carried out and their dynamics for this period was modeled. The following issues are discussed: (a) the nature of the social mood' shocks in Russia, based on their connection with the concepts of politics ...
Added: May 14, 2026
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
Kim J., Lee H., Jeon H. et al., , in: CIKM '25: Proceedings of the 34rd ACM International Conference on Information and Knowledge Management.: ACM, 2025. P. 1344–1353.
Directional forecasting in financial markets requires both accuracy and interpretability. Before the advent of deep learning, interpretable approaches based on human-defined patterns were prevalent, but their structural vagueness and scale ambiguity hindered generalization. In contrast, deep learning models can effectively capture complex dynamics, yet often offer limited transparency. To bridge this gap, we propose a ...
Added: November 21, 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
Manivelan S. V., S. Sabarathinam, Thamilmaran K. et al., Physical Review E - Statistical, Nonlinear, and Soft Matter Physics 2025 Vol. 112 No. 1 Article 014202
We present a study of the theoretical Brusselator model with time-delayed self-feedback, demonstrating its ability to induce extreme events when delays in reaction processes significantly influence subsequent dynamics, with and without diffusion. Stability analyses reveal the mechanisms driving this behavior with respect to the delay time. The occurrence of extreme events is validated using various ...
Added: July 2, 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
Ilia Nikitin, Belan S., Physical Review E - Statistical, Nonlinear, and Soft Matter Physics 2024 Vol. 109 No. 5 Article 054117
Optimization of the mean completion time of random processes by restart is a subject of active theoretical research in statistical physics and has long found practical application in computer science. Meanwhile, one of the key issues remains largely unsolved: how to construct a restart strategy for a process whose detailed statistics are unknown to ensure ...
Added: November 4, 2024
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
Lubashevsky I., Lubashevskiy V., Computer Research and Modeling 2024 Vol. 16 No. 1 P. 79–87
We present a novel model for the dynamical trap of the stimulus-response type that mimics human control over dynamic systems when the bounded capacity of human cognition is a crucial factor. Our focus lies on scenarios where the subject modulates a control variable in response to a certain stimulus. In this context, the bounded capacity ...
Added: February 25, 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
Sviyazov V., Экономический журнал Высшей школы экономики 2023 Т. 27 № 3 С. 412–434
The problem of volatility forecasting with and without consideration of weekly seasonality effect (the weekend effect) is examined in this research. The question of the seasonality existence is understood in the following sense: do models, which incorporate seasonality, feature better forecasts? The fuzzy GARCH model, which accounts for a weekly seasonality effect is presented in ...
Added: October 28, 2023
Mustafin A., Вопросы экономики 2023 № 11 С. 109–122
В статье систематизированы архивные данные о ценах на бумагу и краски в России за 1710—1780-е годы, рассмотрено развитие их производства в стране, предложены ответы на ряд дискуссионных вопросов экономической истории. Исследование основано на материалах более 160 архивных источников, которые позволили построить временные ряды. Полученная динамика цен ставит под сомнение точку зрения о «революции цен» в ...
Added: September 10, 2023
Свиязов В. А., Проблемы управления 2022 № 6 С. 26–34
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: May 15, 2023
Lukianchenko P., Gromov V., Beschastnov Y. et al., Вестник кибернетики 2022 Т. 4 № 48 С. 37–48
The study analyzes the time series of the number of new cases in the administrative courts
of the Russian Federation using two methods of time series grouping according to the chaotic, stochastic, and
regular structure. The first model is based on the entropy‒complexity plane, the second one is presented by the
attribute‒object graph. As a result, four groups ...
Added: March 20, 2023