arXiv AI
In a Streaming World, Should You Stand Still? A Comprehensive Benchmark of Anomaly Detection in Streams
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
3d ago
What Streaming Anomaly Detection Finds (and Misses) in Industrial Time Series
arXiv:2609.39232v1 Announce Type: cross Abstract: EDF relies on continuous monitoring of its power plants to detect anomalies as soon as they occur. Given the absence of a universally optimal streami...
arXiv Machine Learning
Jun 10
Disjoint or Overlapping? Inference Windowing for Reconstruction-Based Time Series Anomaly Detection
arXiv:2606. 09874v1 Announce Type: new Abstract: Reconstruction-based methods are widely used for time series anomaly detection, where models are trained to reconstruct subsequences, and anomalies are identified through reconstruction errors.
arXiv Machine Learning
Jun 9
A Framework for Evaluating and Benchmarking Concept Drift Detection Methods
arXiv:2606. 07789v1 Announce Type: new Abstract: Data stream mining is fundamentally challenged by concept drift, where distributional changes can degrade model performance.
arXiv Machine Learning
Sep 22
Collaborative Streaming Anomaly Detection with Interactive Explanations and Ensemble Consensus
arXiv:2609.23883v1 Announce Type: new Abstract: We present a collaborative streaming anomaly detection system for high-speed data streams that explicitly integrates human analysts into the decision l...
arXiv Machine Learning
Jun 5
Cluster-Aware Causal Mixer for Online Anomaly Detection in Multivariate Time Series
arXiv:2506. 00188v2 Announce Type: replace Abstract: Early and accurate detection of anomalies in time-series data is critical due to the substantial risks associated with false or missed detections.
arXiv AI
Jul 2
PaAno: Patch-Based Representation Learning for Time-Series Anomaly Detection
arXiv:2602. 01359v3 Announce Type: replace-cross Abstract: Although recent studies on time-series anomaly detection have increasingly adopted ever-larger neural network architectures such as transformers and foundation models, they incur high computational costs and memory usage, making them impractical for real-time and resource-constrained scenarios.