Forecasting gold price with the XGBoost algorithm and SHAP interaction values
Forecasting gold price with the XGBoost algorithm and SHAP interaction values
About this item
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Author / Creator
Publisher
New York: Springer US
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Language
English
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Publisher
New York: Springer US
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Scope and Contents
Contents
Financial institutions, investors, mining companies and related firms need an effective accurate forecasting model to examine gold price fluctuations in order to make correct decisions
.
This paper proposes an innovative approach to accurately forecast gold price movements and to interpret predictions. First, it compares six machine learning...
Alternative Titles
Full title
Forecasting gold price with the XGBoost algorithm and SHAP interaction values
Authors, Artists and Contributors
Author / Creator
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Primary Identifiers
Record Identifier
TN_cdi_hal_primary_oai_HAL_hal_03331805v1
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_hal_primary_oai_HAL_hal_03331805v1
Other Identifiers
ISSN
0254-5330
E-ISSN
1572-9338
DOI
10.1007/s10479-021-04187-w
