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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.
May 25, 2026
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?

Time series momentum: Evidence from the European equity market

Heliyon. 2023. Vol. 9. Article e12989.

This study empirically analyzes time series momentum (TSM) in the European equity market
between 2000 & 2020. The study produces additional evidence on TSM where a significant and
persistent market price anomaly enables investors to earn abnormal returns. To achieve this goal
the present study implements a pooled autoregressive model to test the predictability power of
European equity indices of future returns. The results indicate that strategies based on TSM are in
line with the discussed literature and enable market agents to earn returns above the market
(0.71% per month) by using a six-factor model.

Language: English
Full text
DOI
Text on another site
Keywords: autoregressionasset allocation Factor modelsTime series momentumPrice anomaly
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