Please use this identifier to cite or link to this item:
https://elib.bsu.by/handle/123456789/306226| Title: | Time series forecasting using gradient boosting algorithms |
| Authors: | Barysheva, Iolanta Vasilevsky, Konstantin |
| 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. 176-179. |
| 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 |
| URI: | https://elib.bsu.by/handle/123456789/306226 |
| ISBN: | 978-985-881-522-6 |
| Licence: | info:eu-repo/semantics/openAccess |
| Appears in Collections: | 2023. Pattern Recognition and Information Processing (PRIP’2023). Artificial Intelliverse: Expanding Horizons |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 176-179.pdf | 344,75 kB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.