Title: Self-evolving data collection through analytics and business intelligence to predict the price of cryptocurrency

Authors: Adam C. Moyer; William A. Young II; Timothy J. Haase

Addresses: Analytics and Information Systems Department, College of Business, Ohio University, Athens, OH 45701, USA ' Analytics and Information Systems Department, College of Business, Ohio University, Athens, OH 45701, USA ' Economics Department, Anisfield School of Business, Ramapo College of New Jersey, Mahwah, NJ 07430, USA

Abstract: This paper presents the self-evolving data collection engine through analytics and business intelligence (SEDCABI) for predicting Bitcoin prices. Traditionally models use either structured or unstructured data alone, limiting effectiveness. This research pioneers using both data types. SEDCABI harnesses analytics and BI to extract insights from structured historical price and market data. It also incorporates unstructured social media sentiment and news to capture Bitcoin perceptions. Experiments show integrating both data types significantly improves prediction accuracy. SEDCABI continuously adapts to the dynamic crypto market. The plug-in prediction module (PPM) enables customisation. Overall, SEDCABI offers robust Bitcoin price predictions by combining structured and unstructured data. This contributes to cryptocurrency prediction research with an innovative approach to informed decision-making.

Keywords: SEDCABI; self-evolving data collection engine through analytics and business intelligence; prediction; Bitcoin; cryptocurrency; text mining; analytics; business intelligence; unstructured data; sentiment; price.

DOI: 10.1504/IJDS.2025.144833

International Journal of Data Science, 2025 Vol.10 No.1, pp.1 - 26

Received: 20 Nov 2023
Accepted: 06 Apr 2024
Published online: 04 Mar 2025 *

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