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An Experimental Evaluation of Anomaly Detection in Time Series

Summary: Provides a taxonomy of time-series anomaly detectors and a unified empirical comparison of 17 state-of-the-art algorithms on real and synthetic benchmarks using both point and range metrics. Thoroughly evaluates effectiveness, efficiency, and robustness across anomaly rates, data size, dimensionality, anomaly patterns, and threshold settings, and offers practical guidance for method selection. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13725
Venue
VLDB
Year
2024
Pagerank
5.0603878e-05
Overall Rank
6,440 | 55.20%
DOI
10.14778/3620393.3632110

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