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Please use this identifier to cite or link to this item: https://elib.bsu.by/handle/123456789/306259
Title: Iterative Selection of Essential Input Features under Conditions of their Multicollinearity in Space Weather Time Series Forecasting
Authors: Shchurov, Nikolay
Isaev, Igor
Barinov, Oleg
Myagkova, Irina
Dolenko, Sergei
Keywords: ЭБ БГУ::ЕСТЕСТВЕННЫЕ И ТОЧНЫЕ НАУКИ::Кибернетика
ЭБ БГУ::ЕСТЕСТВЕННЫЕ И ТОЧНЫЕ НАУКИ::Математика
Issue Date: 2023
Publisher: Minsk : BSU
Citation: Pattern Recognition and Information Processing (PRIP’2023). Artificial Universe: New Horisont : Proceedings of the 16 th International Conference, Belarus, Minsk, October 17–19, 2023 / Belarusian State University : eds. A. Nedzved, A. Belotserkovsky. – Minsk : BSU, 2023. – Pp. 316-319.
Abstract: The paper presents a method for selecting essential input features when predicting the geomagnetic Dst index, based on iterative selection of features with the highest correlation with respect to the target variable and exclusion of features with high cross-correlation. The models were trained on data from October 1997 to 2017. The criterion for the quality of the forecast using selected features was the root-mean squared error of the Dst index forecast based on the selected set of features on independent data (2018-2022)
URI: https://elib.bsu.by/handle/123456789/306259
ISBN: 978-985-881-522-6
Sponsorship: This study has been performed at the expense of the grant of the Russian Science Foundation, project no. 23-21-00237, https://rscf.ru/en/project/23-21-00237/.
Licence: info:eu-repo/semantics/openAccess
Appears in Collections:2023. Pattern Recognition and Information Processing (PRIP’2023). Artificial Intelliverse: Expanding Horizons

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