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May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
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The Application Efficiency of the Hurst Exponent for the Stocks Prices Forecast

P. 1–5.
Sizykh N., Sizykh D.

The possibility of increasing the accuracy of forecasting stock prices by using the Hurst exponent as an additional indicator to modern forecasting methods. It was obtained confirmation of the effectiveness of the Hurst exponent application as an additional instrument for risk assessment, which allows to improve the reliability of the forecast data in large-scale investment systems. The study (more than 50 companies for the period 2016-2021) confirmed that the application of the Hurst exponent can improve forecasting results of the stock prices.

Language: English
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Keywords: временные рядыHurst exponentпоказатель ХерстаStock pricesцены акцийtime series forecasting methodsinvestment systemинвестиционная система

In book

2022 15th International Conference Management of large-scale system development (MLSD)
M.: IEEE, 2022.
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