Smart Anomaly Detection for Monetization Metrics: A Technical Deep Dive

The anomaly detection system represents a significant advancement in monitoring monetization metrics, addressing the challenges of complex digital transactions. This intelligent framework leverages machine learning and Meta Prophet forecasting for real-time monitoring across multiple dimensions. By implementing sophisticated data processing pipelines and intelligent alert management, the system enables organizations to identify and respond to anomalies quickly while minimizing false positives. The implementation demonstrates significant improvements in detection accuracy, operational efficiency, and business value through automated monitoring and smart alert management. Built on scalable architecture, the system processes millions of data points daily while maintaining performance integrity. It incorporates dynamic threshold adjustments based on historical patterns, seasonal variations, and business context, ensuring relevance across varying transaction volumes. The framework's modular design facilitates seamless integration with existing business intelligence systems, providing actionable insights to stakeholders through customizable dashboards and comprehensive reporting capabilities.

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Publication Details

Journal
Journal of Computer Science and Technology Studies
Published
2025-05-17
DOI
https://doi.org/10.32996/jcsts.2025.7.4.62
Primary Topic
Anomaly Detection Techniques and Applications
Type
article
Field-Weighted Citation Impact
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article

Smart Anomaly Detection for Monetization Metrics: A Technical Deep Dive

Kaarthikshankar Palraj
Journal of Computer Science and Technology Studies
Anomaly Detection Techniques and Applications
article

Smart Anomaly Detection for Monetization Metrics: A Technical Deep Dive

Kaarthikshankar Palraj
article en

Abstract

The anomaly detection system represents a significant advancement in monitoring monetization metrics, addressing the challenges of complex digital transactions. This intelligent framework leverages machine learning and Meta Prophet forecasting for real-time monitoring across multiple dimensions. By implementing sophisticated data processing pipelines and intelligent alert management, the system enables organizations to identify and respond to anomalies quickly while minimizing false positives. The implementation demonstrates significant improvements in detection accuracy, operational efficiency, and business value through automated monitoring and smart alert management. Built on scalable architecture, the system processes millions of data points daily while maintaining performance integrity. It incorporates dynamic threshold adjustments based on historical patterns, seasonal variations, and business context, ensuring relevance across varying transaction volumes. The framework's modular design facilitates seamless integration with existing business intelligence systems, providing actionable insights to stakeholders through customizable dashboards and comprehensive reporting capabilities.

Journal of Computer Science and Technology StudiesVol. 7(4)
University of Florida (US)
Openalex Percentile: Top 95%
Anomaly Detection Techniques and Applications
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