| DC Field | Value | Language |
|---|
| dc.contributor.author | Shchurov, N. | - |
| dc.contributor.author | Isaev, I. | - |
| dc.contributor.author | Barinov, O. | - |
| dc.contributor.author | Myagkova, I. | - |
| dc.contributor.author | Dolenko, S. | - |
| dc.coverage.spatial | Минск | en_US |
| dc.date.accessioned | 2024-02-22T11:24:27Z | - |
| dc.date.available | 2024-02-22T11:24:27Z | - |
| dc.date.issued | 2023 | - |
| dc.identifier.citation | Iterative Selection of Essential Input Features under Conditions of their Multicollinearity in Space Weather Time Series Forecasting / N. Shchurov [et al.] // Pattern Recognition and Information Processing (PRIP'2023) = Распознавание образов и обработка информации (2023) : Proceedings of the 16th International Conference, October 17–19, 2023, Minsk, Belarus / United Institute of Informatics Problems of the National Academy of Sciences of Belarus. – Minsk, 2023. – P. 316–319. | en_US |
| dc.identifier.uri | https://libeldoc.bsuir.by/handle/123456789/54325 | - |
| dc.description.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). | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | BSU | en_US |
| dc.subject | материалы конференций | en_US |
| dc.subject | multivariate time series | en_US |
| dc.subject | prediction | en_US |
| dc.subject | feature selection | en_US |
| dc.subject | artificial neural networks | en_US |
| dc.subject | Earth’s magnetosphere | en_US |
| dc.subject | geomagnetic Dst index | en_US |
| dc.title | Iterative Selection of Essential Input Features under Conditions of their Multicollinearity in Space Weather Time Series Forecasting | en_US |
| dc.type | Article | en_US |
| Appears in Collections: | Pattern Recognition and Information Processing (PRIP'2023) = Распознавание образов и обработка информации (2023)
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