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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.
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Electronics of the Future: Why Superconductors and Spintronics Work Together
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Using Resources of Supercomputing Centers with Everest Platform

P. 687–698.
Smirnov S., Sukhoroslov O. V., Voloshinov V.

High-performance computing plays an increasingly important role in modern science and technology. However, the lack of convenient interfaces and automation tools greatly complicates the widespread use of HPC resources among scientists. The paper presents an approach to solving these problems relying on Everest, a web-based distributed computing platform. The platform enables convenient access to HPC resources by means of domain-specific computational web services, development and execution of many-task applications, and pooling of multiple resources for running distributed computations. The paper describes the improvements that have been made to the platform based on the experience of integration with resources of supercomputing centers. The use of HPC resources via Everest is demonstrated on the example of loosely coupled many-task application for solving global optimization problems.

Language: English
DOI
Keywords: distributed computingweb servicesglobal optimizationHigh-Performance Computing (HPC)many-task applications

In book

Supercomputing. RuSCDays 2018. Communications in Computer and Information Science, vol 965. Springer, Cham
Springer, 2019.
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