Tracking the Unseen: AI-Driven Dashboards for Real-Time Detection of Calendar Anomalies in Cryptocurrency Markets
This study introduces a novel AI-powered Business Intelligence Dashboard System (AIBIDS) designed to detect and visualize calendar-based anomalies in cryptocurrency returns. Focusing on Bitcoin as a case study, the system integrates unsupervised machine learning algorithms to identify periods of abnormal market behavior across multiple temporal resolutions. The proposed system leverages a star-schema OLAP data warehouse, enabling real-time anomaly detection, dynamic visualization, and drill-down exploration of market irregularities. Empirical results confirm the presence of pronounced calendar effects in Bitcoin returns, such as heightened anomalies during Q1 and Q4, and reveal model-specific sensitivities to local versus global volatility. Our novel platform offers a practical, scalable innovation for investors, analysts, and regulators seeking to monitor cryptocurrency markets more effectively, and contributes to the emerging FinTech literature on AI-driven anomaly detection and behavioral market dynamics.
Authors
- Elroi Hadad (ORCID: https://orcid.org/0000-0001-5808-7098)
- Dima Alberg (ORCID: https://orcid.org/0000-0002-8233-8309)
Institutions
- Sami Shamoon College of Engineering (IL)
Publication Details
- Journal
- Journal of risk and financial management
- Published
- 2025-12-12
- DOI
- https://doi.org/10.3390/jrfm18120712
- Primary Topic
- Stock Market Forecasting Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00