Predicting Stock Trends Using Web Semantics and Feature Fusion
Predicting Stock Trends Using Web Semantics and Feature Fusion
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Publisher
Hershey: IGI Global
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Language
English
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Publisher
Hershey: IGI Global
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Scope and Contents
Contents
Stock data are characterized by high dimensionality and sparsity, making stock trend prediction highly challenging. Although the Light Gradient Boosting Machine (LightGBM), based on web semantics, excels at capturing global features and efficiently performs in stock trend prediction, it does not consider the issue of declining prediction performanc...
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Full title
Predicting Stock Trends Using Web Semantics and Feature Fusion
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Author / Creator
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TN_cdi_proquest_journals_3085937896
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_3085937896
Other Identifiers
ISSN
1552-6283
E-ISSN
1552-6291
DOI
10.4018/IJSWIS.346378
