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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.

 

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Изучение энергетической устойчивости регионов Российской Федерации с применением методов анализа паттернов

Управление большими системами: сборник трудов. 2021. № 92. С. 43–63.
Myachin A. L., Prokofiev V. N., Степанов А. А.

The study is devoted to the application of ordinal-fixed and ordinal-invariant pattern clustering to study the structure of the energy sector in regions of Russian Federation over a five-year period. Methods for pattern analysis in the work are due to the independence of the final results from the difference in the absolute values of indicators and the possibility of combining regions that are similar in structure (based on the indicators under consideration). The choice of ordinal-invariant pattern clustering is due to both the endogenous determination of not only the composition of each pattern, but their number, and the lack of the need to choose the initial sequence of the studied indicators, to which some methods of pattern analysis are sensitive. The study was carried out on the basis of such indicators as: a decrease in the level of electricity losses in the networks, the electric power supply of workers, the volume of electricity produced, specific fuel consumption, the cost of services for technological connection, as well as the number of actual connections. As a result of the study, the features of the energy development of individual subjects were studied. The development patterns were obtained, reflecting the similarity of the internal data structure. Dynamic trajectories of development have been built and groups of subjects have been identified that adhere to a constant strategy from year to year. The above results can be taken into account in the development of state policy in the field of energy effi-ciency.

Priority areas: humanitarian IT and mathematics
Language: Russian
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Keywords: cluster analysisкластерный анализpattern analysisанализ паттерновenergy Sustainabilitypatternпаттернэнергетическая устойчивость
Publication based on the results of:
Decision making and data analysis in socio-economic and political systems (2020)
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