Скрытые марковские модели и MPS-временные ряды

З. И. Бежаева; Олехова Е. Ф.; Оселедец В. И.

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July 24, 2026

‘I Like Self-Fulfilling Prophecies

Andrey Vorchik studies happiness, delivers popular science lectures, and believes that science should address social issues as well. In an interview for the Young Scientists of HSE University project, he spoke about how emotions influence decision-making, the Bermuda Triangle formed by the bathroom, refrigerator, and bed, and the ideal formula for education.

July 24, 2026

'Physics Is What the World Is Literally Built On'

Physicist Nina Dzhanayeva, recipient of a Vladimir Potanin Foundation scholarship, focuses her research on nanophotonics. In this interview for the HSE Young Scientists project, she discusses nanowells, scientific intuition, and how physics can help in making frangipane cream puffs.

July 20, 2026

Scientists Create Open Dataset for Studying Concentration

A team of Russian researchers, including scientists from HSE University–St Petersburg, has developed the first open multimodal dataset containing recordings of brain activity, heart function, and video observations to help researchers understand what happens in the human brain during deep concentration. In the future, the dataset could accelerate the development of neural interfaces, rehabilitation technologies, and AI systems. The article has been published in Scientific Data.

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Скрытые марковские модели и MPS-временные ряды

С. 89–95.

Bezhaeva Z., Олехова Е. Ф., Оселедец В. И.

In the paper consider explicit exemple of MPS time series and its cjnnection with hidden markov processes

Language: Russian

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Keywords: Matrix product state hidden markov processes MPS-модели Скрытые марковские временные ряды

In book

Материалы конференции «Современная математика и концепции инновационного математического образования»

М.: ООО "Издательский дом МФО", 2014.

A Clustering Model for Stocks that Considers Hidden Dynamics and Price Trajectory

Morychev G., Sizykh D., Sizykh N., IEEE Access 2025 Vol. 13 P. 213194–213210

One of the main tools for analyzing large volumes of financial data is the use of clustering methods and models, which allow the identification of various patterns. This study examines the problem of clustering time series that reflect the behavior of prices, yields, modes, trends, and a number of related stock indicators. The relevance and ...

Added: February 3, 2026

Low-rank Riemannian eigensolver for high-dimensional Hamiltonians

Rakhuba Maxim, Novikov A., Oseledets I., Journal of Computational Physics 2019 Vol. 396 P. 718–737

Such problems as computation of spectra of spin chains and vibrational spectra of molecules can be written as high-dimensional eigenvalue problems, i.e., when the eigenvector can be naturally represented as a multidimensional tensor. Tensor methods have proven to be an efficient tool for the approximation of solutions of high-dimensional eigenvalue problems, however, their performance deteriorates quickly ...

Added: October 19, 2020