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Iterative Selection of Essential Input Features under Conditions of their Multicollinearity in Space Weather Time Series Forecastingстатья Исследовательская статья Электронная публикация
- Авторы: Shchurov N., Isaev I., Barinov O., Myagkova I., Dolenko S.
- Сборник: Pattern Recognition and Information Processing (PRIP’2023). Artificial Universe: New Horisont. Proceedings of the 16th International Conference, October 17–19, 2023
- Год издания: 2023
- Место издания: Belarusian State University Belarus, Minsk
- Первая страница: 316
- Последняя страница: 319
- Аннотация: 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).
- Добавил в систему: Доленко Сергей Анатольевич
Прикрепленные файлы
| № | Имя | Описание | Имя файла | Размер | Добавлен |
|---|---|---|---|---|---|
| 1. | Полный текст | Оттиск электронной публикации | Shchurov_PRIP-23_offprint.pdf | 412,3 КБ | 20 ноября 2023 [Dolenko] |