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dc.contributor.authorSingh, Manohar
dc.contributor.authorMeher, Bharat Kumar
dc.contributor.authorBirau, Ramona
dc.contributor.authorAnand, Abhishek
dc.date.accessioned2024-08-12T09:05:29Z
dc.date.available2024-08-12T09:05:29Z
dc.date.issued2024
dc.identifier.issn2199-8531
dc.identifier.urihttps://thuvienso.hoasen.edu.vn/handle/123456789/15581
dc.description.tableofcontentsJournal of Open Innovation: Technology, Market, and Complexity, Vol.10, 2024; P. 1-10 https://doi.org/10.1016/j.joitmc.2023.100180vi
dc.language.isoenvi
dc.publisherElseviervi
dc.subjectForecastingvi
dc.subjectRandom Forestvi
dc.subjectHigh-frequency datavi
dc.subjectPythonvi
dc.titleForecasting stock prices of fintech companies of India using random forest with high-frequency datavi
dc.typeArticlevi


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