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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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Brain Signatures of New (Pseudo-) Words: Visual Repetition in Associative and Non-Associative Contexts

Frontiers in Human Neuroscience. 2018. No. 12.
In press

The contribution of the two different training contexts to online, gradual lexical acquisition (ERPs) elicited by new, word-like stimuli. Pseudowords were repeatedly preceded by a picture of a well-known object (semantic-associative training context), or by a hash mark (non-associative training context). The two training styles revealed differential effects of repetition in both behavioural and ERPs data. Repetition of pseudowords not associated with any stimulus gradually enhanced the late positive component (LPC) as well as speeded the lexical categorization of these stimuli, suggesting the formation of episodic memory traces. However, the repetition under the semantic-associative context of the recovery in N400 component and categorization latencies.

Language: English
DOI
Text on another site
Keywords: readingN400LPCcluster-based permutation analysis xregression-based ERPs
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