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August 3, 2026Open Access

In a Streaming World, Should You Stand Still? A Comprehensive Benchmark of Anomaly Detection in Streams

Magali Parrino Antoine Ajenjo Emmanuel Remy Pierre Stephan Pierre Senellart Paul Boniol

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Key Points

The aim is to evaluate different methods of anomaly detection in streaming data to identify the most effective techniques.
Comparative analysis of various anomaly detection algorithms
Evaluation metrics include precision, recall, and F1-score
Applied across multiple streaming datasets for robustness
Identified top-performing algorithms in streaming contexts
Highlighted strengths and weaknesses of each method
Showcased significant improvements in detection rates with certain algorithms

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Implication

Comprehensive benchmark analyzes real-time anomaly detection techniques in streaming data, suggesting improvements in precision.

Abstract

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Cite This Study

Parrino et al. (2026) studied this question.

synapsesocial.com/papers/6a323e06d50b63ecad2076c4 https://doi.org/10.1145/3770855.3817495

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