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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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Model-independent measurement of charm mixing parameters in ¯B→D0(→K0Sπ+π−)μ−¯νμX decays

Physical Review D - Particles, Fields, Gravitation and Cosmology. 2023. Vol. 108. No. 5. Article 052005.
Aaij R., Abdelmotteleb A. S., Abellan Beteta C., Abudinén F., Ackernley T., Boldyrev A., Hushchyn M., Karpov M., Mokhnenko S., Ratnikov F., Ryzhikov A.

https://journals.aps.org/prd/abstract/10.1103/PhysRevD.108.052005

Research target: Physics Computer Science
Language: English
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DOI
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Keywords: физика высоких энергийLarge Hadron ColliderБольшой адронный коллайдерHigh Energy Physics - Experiment
Publication based on the results of:
Application of neuromorphic approaches to optimization problems for complicated systems for physics experiments (2023)
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