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Windowed mass-ratio-variance-based outlier factor for contextual anomaly detection in data streams

algorithms-and-analysis-of-algorithms
artificial-intelligence
data-mining-and-machine-learning
data-science
neural-networks

Abstract

Real-time anomaly detection in data streams plays a critical role in uncovering unusual patterns, abrupt changes, and unexpected contextual deviations. This paper introduces the Windowed Mass-Ratio-Variance based Outlier Factor (WMOF) algorithm, specifically designed for streaming data analysis. WMOF addresses the inherent challenges of streaming anomaly detection by employing an overlapping sliding window model and a parameter-free anomaly scoring submodule. It leverages the concept of density-based outlier factors and the specific threshold to assign anomaly points within the window. WMOF effectively identifies anomalies within each window without requiring explicit model assumptions. This modelless AI enhances the adaptability and robustness of WMOF to various data distributions. The performance of WMOF is extensively evaluated against existing algorithms - including Isolated Forest, Local Outlier Factor, One-Class SVM, and KNN from the PySAD library on 14 diverse datasets. The results demonstrate that WMOF achieves superior effectiveness and efficiency in streaming anomaly detection. This underscores its significant potential for real-world applications demanding real-time anomaly identification. The source code for this work is publicly available on GitHub: https://github.com/oakkao/pymof
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