Time series forecasting using gradient boosting algorithms

Please use this identifier to cite or link to this item: https://libeldoc.bsuir.by/handle/123456789/54369

Title:	Time series forecasting using gradient boosting algorithms
Authors:	Barysheva, I. Vasilevsky, K.
Keywords:	материалы конференций;gradient boosting;financial time series forecasting;XGBoost
Issue Date:	2023
Publisher:	BSU
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.
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://libeldoc.bsuir.by/handle/123456789/54369
Appears in Collections:	Pattern Recognition and Information Processing (PRIP'2023) = Распознавание образов и обработка информации (2023)

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