Year: 2025
Type: article
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.
Author: Kaarthikshankar Palraj
Institution: University of Florida
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4410535378