Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386457795> ?p ?o ?g. }
Showing items 1 to 89 of
89
with 100 items per page.
- W4386457795 endingPage "3813" @default.
- W4386457795 startingPage "3813" @default.
- W4386457795 abstract "This article presents a study on forecasting silver prices using the extreme gradient boosting (XGBoost) machine learning method with hyperparameter tuning. Silver, a valuable precious metal used in various industries and medicine, experiences significant price fluctuations. XGBoost, known for its computational efficiency and parallel processing capabilities, proves suitable for predicting silver prices. The research focuses on identifying optimal hyperparameter combinations to improve model performance. The study forecasts silver prices for the next six days, evaluating models based on mean absolute percentage error (MAPE) and root mean square error (RMSE). Model A (the best model based on MAPE value) suggests silver prices decline on the first and second days, rise on the third, decline again on the fourth, and stabilize with an increase on the fifth and sixth days. Model A achieves a MAPE of 5.98% and an RMSE of 1.6998, utilizing specific hyperparameters. Conversely, model B (the best model based on RMSE value) indicates a price decrease until the third day, followed by an upward trend until the sixth day. Model B achieves a MAPE of 6.06% and an RMSE of 1.6967, employing distinct hyperparameters. The study also compared the proposed models with several other ensemble models (CatBoost and random forest). The model comparison was carried out by incorporating 2 additional metrics (MAE and SI), and it was found that the proposed models exhibited the best performance. These findings provide valuable insights for forecasting silver prices using XGBoost." @default.
- W4386457795 created "2023-09-06" @default.
- W4386457795 creator A5072259200 @default.
- W4386457795 creator A5074227149 @default.
- W4386457795 creator A5092759675 @default.
- W4386457795 date "2023-09-05" @default.
- W4386457795 modified "2023-09-27" @default.
- W4386457795 title "Silver Price Forecasting Using Extreme Gradient Boosting (XGBoost) Method" @default.
- W4386457795 cites W2015946628 @default.
- W4386457795 cites W2154328662 @default.
- W4386457795 cites W2796358601 @default.
- W4386457795 cites W2889939530 @default.
- W4386457795 cites W2911517174 @default.
- W4386457795 cites W2927201630 @default.
- W4386457795 cites W2945137668 @default.
- W4386457795 cites W2946737604 @default.
- W4386457795 cites W2976353133 @default.
- W4386457795 cites W3045004532 @default.
- W4386457795 cites W3102476541 @default.
- W4386457795 cites W3110870618 @default.
- W4386457795 cites W3122159869 @default.
- W4386457795 cites W3130219998 @default.
- W4386457795 cites W3135114506 @default.
- W4386457795 cites W3138239626 @default.
- W4386457795 cites W3155739706 @default.
- W4386457795 cites W3184255274 @default.
- W4386457795 cites W4205913202 @default.
- W4386457795 cites W4214860529 @default.
- W4386457795 cites W4220803424 @default.
- W4386457795 cites W4283775449 @default.
- W4386457795 cites W4292457660 @default.
- W4386457795 cites W4293565759 @default.
- W4386457795 cites W4361804670 @default.
- W4386457795 cites W4382450234 @default.
- W4386457795 cites W92469554 @default.
- W4386457795 doi "https://doi.org/10.3390/math11183813" @default.
- W4386457795 hasPublicationYear "2023" @default.
- W4386457795 type Work @default.
- W4386457795 citedByCount "0" @default.
- W4386457795 crossrefType "journal-article" @default.
- W4386457795 hasAuthorship W4386457795A5072259200 @default.
- W4386457795 hasAuthorship W4386457795A5074227149 @default.
- W4386457795 hasAuthorship W4386457795A5092759675 @default.
- W4386457795 hasBestOaLocation W43864577951 @default.
- W4386457795 hasConcept C105795698 @default.
- W4386457795 hasConcept C119857082 @default.
- W4386457795 hasConcept C139945424 @default.
- W4386457795 hasConcept C149782125 @default.
- W4386457795 hasConcept C150217764 @default.
- W4386457795 hasConcept C154945302 @default.
- W4386457795 hasConcept C169258074 @default.
- W4386457795 hasConcept C33923547 @default.
- W4386457795 hasConcept C41008148 @default.
- W4386457795 hasConcept C46686674 @default.
- W4386457795 hasConcept C70153297 @default.
- W4386457795 hasConcept C8642999 @default.
- W4386457795 hasConceptScore W4386457795C105795698 @default.
- W4386457795 hasConceptScore W4386457795C119857082 @default.
- W4386457795 hasConceptScore W4386457795C139945424 @default.
- W4386457795 hasConceptScore W4386457795C149782125 @default.
- W4386457795 hasConceptScore W4386457795C150217764 @default.
- W4386457795 hasConceptScore W4386457795C154945302 @default.
- W4386457795 hasConceptScore W4386457795C169258074 @default.
- W4386457795 hasConceptScore W4386457795C33923547 @default.
- W4386457795 hasConceptScore W4386457795C41008148 @default.
- W4386457795 hasConceptScore W4386457795C46686674 @default.
- W4386457795 hasConceptScore W4386457795C70153297 @default.
- W4386457795 hasConceptScore W4386457795C8642999 @default.
- W4386457795 hasFunder F4320329092 @default.
- W4386457795 hasIssue "18" @default.
- W4386457795 hasLocation W43864577951 @default.
- W4386457795 hasOpenAccess W4386457795 @default.
- W4386457795 hasPrimaryLocation W43864577951 @default.
- W4386457795 hasRelatedWork W3084206917 @default.
- W4386457795 hasRelatedWork W3160759841 @default.
- W4386457795 hasRelatedWork W3177321454 @default.
- W4386457795 hasRelatedWork W3208169454 @default.
- W4386457795 hasRelatedWork W4285733885 @default.
- W4386457795 hasRelatedWork W4309717779 @default.
- W4386457795 hasRelatedWork W4322775603 @default.
- W4386457795 hasRelatedWork W4375930479 @default.
- W4386457795 hasRelatedWork W4379536929 @default.
- W4386457795 hasRelatedWork W4385388583 @default.
- W4386457795 hasVolume "11" @default.
- W4386457795 isParatext "false" @default.
- W4386457795 isRetracted "false" @default.
- W4386457795 workType "article" @default.