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Self-Evolving Data Collection Through Analytics and Business Intelligence to Predict the Price of Cryptocurrency

Moyer, Adam C.
http://orcid.org/0000-0003-2313-0513
http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1604656483616404

Abstract Details

2020, Doctor of Philosophy (PhD), Ohio University, Mechanical and Systems Engineering (Engineering and Technology).
The development of the self-evolving data collection engine through analytics and business intelligence (SEDCABI) research engine along with plug-in prediction module (PPM) is demonstrated for the prediction of cryptocurrency (specifically, Bitcoin). Leveraging all data proves increase the accuracy of the prediction when compared to using only structured data, or only using unstructured data alone.
Gary Weckman (Advisor)
84 p.
Information Science; Information Systems
SEDCABI; analytics; business intelligence; ETL; predictive analytics; RMM; rolling model method; ATST; automated transposition storage technique; PPM; plug-in prediction module; SEDC; self-evolving data collection

Recommended Citations

Citations

  • Moyer, A. C. (2020). Self-Evolving Data Collection Through Analytics and Business Intelligence to Predict the Price of Cryptocurrency [Doctoral dissertation, Ohio University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1604656483616404

    APA Style (7th edition)

  • Moyer, Adam. Self-Evolving Data Collection Through Analytics and Business Intelligence to Predict the Price of Cryptocurrency. 2020. Ohio University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1604656483616404.

    MLA Style (8th edition)

  • Moyer, Adam. "Self-Evolving Data Collection Through Analytics and Business Intelligence to Predict the Price of Cryptocurrency." Doctoral dissertation, Ohio University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1604656483616404

    Chicago Manual of Style (17th edition)

ohiou1604656483616404
641
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This open access ETD is published by Ohio University and OhioLINK.