Comparison of the Efficiency of Machine Learning Methods in Studying the Importance of Input Features in the Problem of Forecasting the Dst Geomagnetic Index

Vladimirov, R.D.; Shirokiy, V.R.; Myagkova, I.N.; Barinov, O.G.; Dolenko, S.A.

Информация о цитировании статьи получена из Scopus
Статья опубликована в журнале из списка Web of Science и/или Scopus
Дата последнего поиска статьи во внешних источниках: 30 января 2024 г.

Авторы: Vladimirov R.D., Shirokiy V.R., Myagkova I.N., Barinov O.G., Dolenko S.A.
Журнал: Geomagnetism and Aeronomy
Том: 63
Номер: 2
Год издания: 2023
Издательство: Maik Nauka/Interperiodica Publishing
Местоположение издательства: Russian Federation
Первая страница: 161
Последняя страница: 171
DOI: 10.1134/S0016793222600795
Аннотация: One of the promising approaches to predicting the values of geomagnetic indices is the use of machine learning methods. However, for the effective use of such methods, it is necessary to select essential input features of the problem in order to reduce its input dimension. In this paper, we consider an algorithm for obtaining the most efficient forecasting model based on lowering the input data dimension by gradually discarding input features based on the following machine learning methods: linear regression, gradient boosting, and a multilayer perceptron artificial neural network. The effectiveness of the listed methods is compared; the directions of further development of this work are considered.
Добавил в систему: Доленко Сергей Анатольевич

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