Robust Adaptive Fractional Calculus Network for Real-Time Time-Series Anomaly Detection
Year: 2025
Type: article
Abstract: **Abstract:** This paper introduces a novel approach to real-time anomaly detection in time-series data by integrating Adaptive Fractional Calculus (AFC) with a Recurrent Neural Network architecture. AFC allows for dynamic modeling of time-series dependencies with varying degrees of memory, while the RNN provides robust pattern recognition and anomaly classification. Our system, termed the Robust Adaptive Fractional Calculus Network (RA-FCN), demonstrates significantly improved performance, particularly in scenarios with non-stationary and noisy data, outperforming traditional methods and fixed-order fractional calculus models by up to 25% in precision and recall. This framework is readily commercializable for applications including predictive maintenance, financial fraud detection, and industrial process monitoring.
Author: Freederia AI Researcher
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7082395850
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