| DC Field | Value | Language |
|---|
| dc.contributor.author | Barysheva, I. | - |
| dc.contributor.author | Vasilevsky, K. | - |
| dc.coverage.spatial | Минск | en_US |
| dc.date.accessioned | 2024-02-26T11:33:46Z | - |
| dc.date.available | 2024-02-26T11:33:46Z | - |
| dc.date.issued | 2023 | - |
| dc.identifier.citation | Barysheva, I. Time series forecasting using gradient boosting algorithms / I. Barysheva, K. Vasilevsky // 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. 176–179. | en_US |
| dc.identifier.uri | https://libeldoc.bsuir.by/handle/123456789/54369 | - |
| dc.description.abstract | This study investigates the efficiency of gradient
boosting algorithms, particularly XGBoost, in time series
forecasting. We optimize the parameters using
RandomizedSearchCV and apply the model to daily stock prices
of the Ethereum cryptocurrency. Additionally, we compare the
prediction performance of XGBoost with two other models,
LightGBM and CatBoost. Our findings reveal that the
LightGBM model outperforms both CatBoost and XGBoost in
terms of accuracy for time series prediction. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | BSU | en_US |
| dc.subject | материалы конференций | en_US |
| dc.subject | gradient boosting | en_US |
| dc.subject | financial time series forecasting | en_US |
| dc.subject | XGBoost | en_US |
| dc.title | Time series forecasting using gradient boosting algorithms | en_US |
| dc.type | Article | en_US |
| Appears in Collections: | Pattern Recognition and Information Processing (PRIP'2023) = Распознавание образов и обработка информации (2023)
|
