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.

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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
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article

Tracking the Unseen: AI-Driven Dashboards for Real-Time Detection of Calendar Anomalies in Cryptocurrency Markets

Elroi Hadad, Dima Alberg
Journal of risk and financial management
Stock Market Forecasting Methods
article

Tracking the Unseen: AI-Driven Dashboards for Real-Time Detection of Calendar Anomalies in Cryptocurrency Markets

Elroi Hadad, Dima Alberg
article en

Abstract

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.

Journal of risk and financial managementVol. 18(12)
Sami Shamoon College of Engineering (IL)
Openalex Percentile: Top 55%
Stock Market Forecasting Methods
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Tracking the Unseen: AI-Driven Dashboards for Real-Time Detection of Calendar Anomalies in Cryptocurrency Markets — Elroi Hadad, Dima Alberg · Journal of risk and financial management (2025) | TGRS Research Map | TGRS