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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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Russian Error-Annotated Learner English Corpus: a Tool for Computer-Assisted Language Learning

P. 87–97.
Kutuzov A. B., Kuzmenko E.

The paper describes the learner corpus composed of English essays written by native Russian speakers. REALEC (Russian Error-Annotated Learner English Corpus) is an error-annotated, available online corpus, now containing more than 200 thousand word tokens in almost 800 essays. It is one of the first Russian ESL corpora, dynamically developing and striving to improve both in size and in features offered to users. We describe our perspective on the corpus, data sources and tools used in compiling it. Elaborate self-made classification of learners’ errors types is thoroughly described. The paper also presents a pilot experiment on creating test sets for particular learners’ problems using corpus data.

Language: English
Full text
Text on another site
Keywords: learner corporacomputer-assisted language learningEnglish as a second language
Publication based on the results of:
Corpus technologies in linguistic and interdisciplinary research (2014)

In book

Proceedings of the third workshop on NLP for computer-assisted language learning at SLTC 2014, Uppsala University
Linköping: Linköping University Electronic Press, 2014.
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