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АНАЛИЗ СТРУКТУРЫ ВРЕМЕННЫХ РЯДОВ КОЛИЧЕСТВА ДЕЛ В СУДЕ
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 of time series were derived: regular, regular-chaotic, purely
chaotic, and chaotic-stochastic. Most of the series turned out to be chaotic-stochastic, which is common for real systems. Each group of time series is assigned with a suitable prediction algorithm. For example, algo-
rithms of nonlinear dynamics can be used for chaotic series, and models based on stochastic processes can be
used for strongly stochastic series.
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
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
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
Ignatov D. I., Lobachevskii Journal of Mathematics 2023 No. 44 P. 137–146
We consider two ways how to compute the number of maximal antichains in the Boolean lattice on 𝑛 elements. The first one is based on full direct enumeration, while the second ones relies on concept lattices or Galois lattices (studied in Formal Concept Analysis, an applied branch of lattice theory) and the Dedekind–MacNeille completion of a partial ...
Added: June 13, 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
Gromov V., Урманцева Н. Р., [б.и.], 2021.
В докладе рассматриваются подходы к прогнозированию на основе кластеризации, опирающиеся на методологию анализа формальных понятий. Методология применяется для кластеризации участков временного ряда с целью выделения характерных участков (мотивов), отвечающих больным с различной степенью засорённости фистулы. ...
Added: January 30, 2023
Ilya Semenkov, Sergei O. Kuznetsov, , in: Proceedings of the 9th International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI 2021)Vol. 2972.: CEUR-WS, 2021. P. 105–112.
This paper presents different versions of classification ensemble methods based on pattern structures. Each of these methods is described and tested on multiple datasets (including datasets with exclusively numerical and exclusively nominal features). As a baseline model Random Forest generation is used. For some classification tasks the classification algorithms based on pattern structures showed better ...
Added: December 19, 2022
Makeeva N., Stankevich I., Экономический журнал Высшей школы экономики 2022 Т. 26 № 4 С. 598–622
The paper discusses the problem of nowcasting the current growth rates of Russian GDP and its components using quarterly data. The quality of restricted and unrestricted MIDAS models (models with mixed data), MIDAS model with L1 regularisation and MFBVAR model (Bayesian vector autoregression of mixed frequency) are compared. The results are compared with classical autoregression ...
Added: December 9, 2022
Pashkov S., Социология: методология, методы, математическое моделирование 2021 № 53 С. 39–82
The Consumer Sentiments Index (CSI) reflects views of the population of Russia on the economic and financial policy of the country and contributes to the understanding of recessive changes in the economy. Current methodological approach singles out inflation, exchange rate, unemployment, intensity of economic events coverage in mass media as the primary factors that guide consumers in their assessments when ...
Added: December 5, 2022
Sizykh N., Orshanskaya E., Sizykh D., , in: 2022 15th International Conference Management of large-scale system development (MLSD).: M.: IEEE, 2022. P. 1–6.
The paper presents the research results of the predictive ability of stock quote forecasting models using the ARIMA/LSTM hybrid model. This study is based on a predictive power analysis using a sample of 30 companies from three sectors: energy, finance, and technology. ...
Added: November 14, 2022
Sizykh N., Sizykh D., , in: 2022 15th International Conference Management of large-scale system development (MLSD).: M.: IEEE, 2022. P. 1–5.
The possibility of increasing the accuracy of forecasting stock prices by using the Hurst exponent as an additional indicator to modern forecasting methods. It was obtained confirmation of the effectiveness of the Hurst exponent application as an additional instrument for risk assessment, which allows to improve the reliability of the forecast data in large-scale investment ...
Added: November 14, 2022
Mustafin A., Древняя Русь. Вопросы медиевистики 2024 Т. 2 С. 78–88
В статье предпринята попытка описать и проанализировать движение цен на бумагу в России за 1580–1710-е гг. Почерпнутые из источников сведения позволили рассмотреть вопрос о причинах изменения цен, о «бумажном голоде» в России. Показано влияние цен на отечественное производство бумаги. Впервые определяется наценка при продаже гербовой бумаги. ...
Added: October 31, 2022