• A
  • A
  • A
  • АБВ
  • АБВ
  • АБВ
  • A
  • A
  • A
  • A
  • A
Обычная версия сайта
  • RU
  • EN
  • HSE University
  • Publications
  • Book chapter
  • Neural Network Modeling and What-if Scenarios: Applications to Various-Term Sales Forecasts
  • RU
  • EN
Расширенный поиск
Высшая школа экономики
Национальный исследовательский университет
Priority areas
  • business informatics
  • economics
  • engineering science
  • humanitarian
  • IT and mathematics
  • law
  • management
  • mathematics
  • sociology
  • state and public administration
by year
  • 2027
  • 2026
  • 2025
  • 2024
  • 2023
  • 2022
  • 2021
  • 2020
  • 2019
  • 2018
  • 2017
  • 2016
  • 2015
  • 2014
  • 2013
  • 2012
  • 2011
  • 2010
  • 2009
  • 2008
  • 2007
  • 2006
  • 2005
  • 2004
  • 2003
  • 2002
  • 2001
  • 2000
  • 1999
  • 1998
  • 1997
  • 1996
  • 1995
  • 1994
  • 1993
  • 1992
  • 1991
  • 1990
  • 1989
  • 1988
  • 1987
  • 1986
  • 1985
  • 1984
  • 1983
  • 1982
  • 1981
  • 1980
  • 1979
  • 1978
  • 1977
  • 1976
  • 1975
  • 1974
  • 1973
  • 1972
  • 1971
  • 1970
  • 1969
  • 1968
  • 1967
  • 1966
  • 1965
  • 1964
  • 1963
  • 1958
  • More
Subject
News
April 28, 2026
Scientists Develop Algorithm for Accurate Financial Time Series Forecasting
Researchers at the HSE Faculty of Computer Science benchmarked more than 200,000 model configurations for predicting financial asset prices and realised volatility, showing that performance can be improved by filtering out noise at specific frequencies in advance. This technique increased accuracy in 65% of cases. The authors also developed their own algorithm, which achieves accuracy comparable to that of the best models while requiring less computational power. The study has been published in Applied Soft Computing.
April 27, 2026
Fair Division: How Mathematics Helps to Divide the Indivisible
How can items be allocated among participants so that no one feels short-changed? Alexander Karpov, Assistant Professor at the Faculty of Economic Sciences, and his Singaporean colleague, Prof. Warut Suksompong, set out to find a mathematical answer to this question. In this interview, they discuss how a model of rational preferences is constructed, why one cannot rely on a simple sum of values, and where an algorithm that asks a minimal number of questions can be useful.
April 24, 2026
Electronics of the Future: Why Superconductors and Spintronics Work Together
It was once believed that superconductivity and magnetism avoided each other like the devil avoids holy water. However, modern nanostructures prove the opposite. A Russian theoretical physicist and Indian experimentalists have joined forces to create the electronics of the future—free from energy losses. Nataliya Pugach, Professor at the School of Electronic Engineering at HSE MIEM and Leading Research Fellow at the Quantum Nanoelectronics Laboratory, explains how a long-standing acquaintance in Cambridge grew into a mirror laboratory project with the Indian Institute of Technology Bombay (IIT Bombay), how superconducting spintronics works, and what surprises a researcher in India beyond the university campus.

 

Have you spotted a typo?
Highlight it, click Ctrl+Enter and send us a message. Thank you for your help!

Publications
  • Books
  • Articles
  • Chapters of books
  • Working papers
  • Report a publication
  • Research at HSE

?

Neural Network Modeling and What-if Scenarios: Applications to Various-Term Sales Forecasts

P. 122–126.
Kuskova V., Zaytsev D., Sokol A., Khvatsky G.
Language: English
Keywords: forecastingNeural Networkpredictive models

In book

Proceedings of the 26th ISSAT Conference on Reliability and Quality in Design
International Society of Science and Applied Technologies, 2021.
Similar publications
Экономическая интеграция России и Беларуси: перспективы роста и обмена инновациями
Kopnova E., Журавлева К. А., Коряков И. В. et al., Журнал Белорусского государственного университета. Экономика 2025 № 1 С. 36–46
he article examines the prospects for economic cooperation between Russia and Belarus within the framework of the EAEU, SCO and BRICS integration associations, with an emphasis on the impact of sanctions and their consequences for the economic growth of the countries. The study includes the use of econometric modeling methods to evaluate the forecast of ...
Added: December 11, 2025
Development the reinforcement learning model for sources identification of H2S industrial emissions
Kychkin A., Chernitsin I., Vikentyeva O., , in: 2025 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM).: IEEE, 2025. P. 987–991.
Industry 4.0 concept focuses on sustainability problem that requires to control air emissions, especially for harmful substances like H2S, and reduction their impact on nature by using environmental monitoring and sources identification systems. This task requires solving inverse problem of dispersion models, which should establish complex mathematical dependences between the sensor data, the location and ...
Added: November 4, 2025
Предикторы годовой выживаемости после ишемического инсульта
Kulikova S., Polyakova I., Kuzmicheva E. et al., Неврология, нейропсихиатрия, психосоматика 2025 Т. 17 № 5 С. 48–54
Predicting the outcome of ischaemic stroke (IS) is a complex task, as mortality and disability depend on many factors, including age, gender, type and severity of stroke, and comorbidities. Survival rates also vary between countries depending on genetic characteristics and differences in the organisation of healthcare systems. Objective: to search for predictors of one-year survival after ...
Added: October 27, 2025
Методы машинного обучения в макроэкономическом прогнозировании: предварительные итоги
Smirnov S. V., Вопросы экономики 2025 № 10 С. 131–154
The paper summarizes machine-learning (ML) methods most relevant to macroeconomics and assesses their performance in forecasting and nowcasting key macro indicators. Despite rapid methodological progress and a surge of publications over the past 25 years, gains in forecast accuracy with traditional statistical (economic, financial, and survey) data remain modest. ML models often outperform naïve and ...
Added: October 12, 2025
System Architecture of the Automated AI-driven Predictive Assessment for Environmental Monitoring in Industrial Areas
Kychkin A., Chernitsin I., Vikentyeva O., , in: 2025 International Russian Automation Conference (RusAutoCon).: IEEE, 2025. P. 1046–1050.
This paper presents system architecture of the automated AI-driven predictive assessment of the ecological state of the air in sanitary protection zones around industrial facilities. The developed solution implements automated pre-processing of data presented in the form of time series using machine learning methods and the construction of short-term forecasts of pollutant concentrations for the ...
Added: October 6, 2025
Анализ факторов, влияющих на управление денежными потоками предприятия
Чан Ф. Т., Староверова О. В., Kosov M., Аудиторские ведомости 2025 № 2 С. 129–134
This article is devoted to the study of factors affecting enterprise cash flow management. Methods of analysis and synthesis of peer-reviewed scientific sources on this issue are applied. As a result of the research, two groups of factors have been identified and systematized: objective factors (state economic policy, customer influence) and subjective factors (competence of ...
Added: September 22, 2025
Роль исторических эпох и символов в моделировании динамики и шоков социальных настроений в России
Karacharovskiy V., Социологические исследования 2025 № 6 С. 3–14
The article evaluates the possibility of forecasting mass social attitudes based on the interest of the population for the eras in the development of the country. Historical eras and their symbols are considered as comparison standards, temporal analogues of reference groups that provide the mass consciousness with samples of “reserve” standards of living and standards ...
Added: August 12, 2025
К прогнозированию вероятности невооруженной революционной дестабилизации методами машинного обучения
Медведев И. А., Korotayev A., Социология власти 2025 Т. 37 № 2 С. 108–141
The authors provide a broad overview of the main applications for machine learning methods in political sociology. They describe history of the transition from simple regression models to complex machine learning models. The reasons for and benefits of this transition are discussed. The authors identify the main uses of machine learning models in related disciplines and ...
Added: August 1, 2025
Predicting Extreme Events for Complex High-Dimensional Systems
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
Development of Image Preprocessing Methods for Software Compensation of Refraction Anomalies of an Observer’s Eyes
Алкзир Н., Yarykina n., Nikolaev D. et al., Neuroscience and Behavioral Physiology 2024
Added: April 28, 2025
Chaotic dynamics in an overlapping generations model: Forecasting and regularization
Tatyana A. Alexeeva, Kuznetsov N., Mokaev T. et al., Chaos, Solitons and Fractals 2025 Vol. 196 Article 116371
Irregular dynamics (especially chaotic) is often undesirable in economics because it presents challenges for predicting and controlling the behavior of economic agents. In this paper, we used an overlapping generations (OLG) model with a control function in the form of government spending as an example, to demonstrate an effective approach to forecasting and regulating chaotic ...
Added: April 20, 2025
Approximating and Predicting Energy Consumption of Portable Devices
Ullah T., Siraj A. H., Umer Mukhtar Andrabi et al., , in: 2022 VIII International Conference on Information Technology and Nanotechnology (ITNT).: IEEE, 2022. P. 1–7.
Added: March 20, 2025
Forecasting market volatility using AI and ML models
Pshichenko D., Znanstvena misel 2024 No. 96 P. 38–42
The article analyzes the application of artificial intelligence (AI) models for forecasting market volatility (MV). Examples of algorithms such as recurrent neural networks (RNN), long short-term memory (LSTM) networks, and regression methods are studied, demonstrating their effectiveness in processing time series and identifying complex data patterns. The importance of integrating machine learning (ML), as a ...
Added: March 10, 2025
Сравнительный анализ моделей прогнозирования региональной инфляции
Габов М. А., Bukina T. V., Kashin D., Журнал Новой экономической ассоциации 2025 № 4(69) С. 87–117
The study aims to compare approaches to forecasting the monthly level of consumer price index (CPI y/y) in the regions of the Volga Federal District using time series models and machine learning methods. This study attempts to select the most appropriate and efficient models for predicting the regional general price level index. The paper also ...
Added: February 22, 2025
Features of Data Collection and Software Tool Architecture for Performing Predictive Analysis of Phenomena Leading to Forest Fires
Hlib Nekrasov, Aleksandr Belov, , in: International IoT, Electronics and Mechatronics Conference, Volume 2. Proceedings of IEMTRONICS 2024. LNEE, volume 1228Vol. 1228.: Springer Publishing Company, 2025. P. 379–395.
Added: January 26, 2025
Анализ процессов образования контактных радиопомех при комплексном обеспечении электромагнитной совместимости на подвижных объектах радиоэлектронных средств
Б.В. Уткин, Н.Н. Грачёв, Журнал Сибирского федерального университета. Серия: Техника и технологии 2024 Т. 17 № 8 С. 1077–1088
The following article presents an analysis of electromagnetic interference formation caused by the secondary electromagnetic re- emissions formed by radio electronic devices. Based on the results of the conducted research, a method for predicting the spectral characteristics of contact interference to a radio receiver is proposed. The methodology for predicting the spectral composition of contact ...
Added: December 28, 2024
Potential of business uncertainty indicators in forecasting economic activity: The case of Russia
Lola I. S., Asoskov D., Russian Journal of Economics 2024 No. 10(4) P. 351–364
This paper investigates the utility of business uncertainty indicators as predictive tools for forecasting economic activity in the context of Russia. In an era characterized by global economic volatility and geopolitical shifts, understanding the dynamics of economic uncertainty and its impact on overall economic performance is of paramount importance. The study utilizes a comprehensive dataset ...
Added: December 10, 2024
Нейросетевой алгоритм выявления и удаления выбросов в зашумленных наборах данных
Yasnitsky L., Plotnikova E. G., Прикладная информатика 2024 Т. 19 № 5 С. 88–100
Outliers in statistical data, which are the result of erroneously collected information, are often an obstacle to the successful application of machine learning methods in many subject areas. The presence of outliers in training data sets reduces the accuracy of machine learning models, and in some cases, makes the application of these methods impossible. Currently ...
Added: November 29, 2024
Exploring the potential of business uncertainty indicators in forecasting economic activity: The case of Russia
Lola I. S., Asoskov D., / NRU Higher School of Economics. Series WP BRP "Science, Technology and Innovation". 2024. No. 128/STI/2024.
This paper investigates the utility of business uncertainty indicators as predictive tools for forecasting economic activity in the context of Russia. In an era characterized by global economic volatility and geopolitical shifts, understanding the dynamics of economic uncertainty and its impact on overall economic performance is of paramount importance. The study utilizes a comprehensive dataset ...
Added: November 2, 2024
  • About
  • About
  • Key Figures & Facts
  • Sustainability at HSE University
  • Faculties & Departments
  • International Partnerships
  • Faculty & Staff
  • HSE Buildings
  • HSE University for Persons with Disabilities
  • Public Enquiries
  • Studies
  • Admissions
  • Programme Catalogue
  • Undergraduate
  • Graduate
  • Exchange Programmes
  • Summer University
  • Summer Schools
  • Semester in Moscow
  • Business Internship
  • Research
  • International Laboratories
  • Research Centres
  • Research Projects
  • Monitoring Studies
  • Conferences & Seminars
  • Academic Jobs
  • Yasin (April) International Academic Conference on Economic and Social Development
  • Media & Resources
  • Publications by staff
  • HSE Journals
  • Publishing House
  • iq.hse.ru: commentary by HSE experts
  • Library
  • Economic & Social Data Archive
  • Video
  • HSE Repository of Socio-Economic Information
  • HSE1993–2026
  • Contacts
  • Copyright
  • Privacy Policy
  • Site Map
Edit