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Dive into Time-Series Anomaly Detection: A Decade Review

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arxiv 2412.20512 v1 pith:AZ6EPNRG submitted 2024-12-29 cs.LG cs.AIcs.DBstat.ML

Paul Boniol , Qinghua Liu , Mingyi Huang , Themis Palpanas , John Paparrizos This is my paper · ORCID
classification cs.LGcs.AIcs.DBstat.ML
keywords anomalydetectiontime-seriesdatageneralliteraturemethodsrecent
verification ladder T0 review T1 audit T2 compute T3 formal
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Recent advances in data collection technology, accompanied by the ever-rising volume and velocity of streaming data, underscore the vital need for time series analytics. In this regard, time-series anomaly detection has been an important activity, entailing various applications in fields such as cyber security, financial markets, law enforcement, and health care. While traditional literature on anomaly detection is centered on statistical measures, the increasing number of machine learning algorithms in recent years call for a structured, general characterization of the research methods for time-series anomaly detection. This survey groups and summarizes anomaly detection existing solutions under a process-centric taxonomy in the time series context. In addition to giving an original categorization of anomaly detection methods, we also perform a meta-analysis of the literature and outline general trends in time-series anomaly detection research.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. PRISM: Powerful Time Series to Image (TS2I) Representations for Multivariate Anomaly Detection

    cs.LG 2026-08 conditional novelty 6.0 of 10

    A new channelization scheme (MSM) and image-based autoencoders make time-series anomaly detection competitive with 24 time-domain baselines on 14 benchmarks.

  2. Real-Time Decorrelation-Based Anomaly Detection for Multivariate Time Series

    cs.LG 2025-07 conditional novelty 6.0 of 10

    DAD detects multivariate time-series anomalies by measuring the change in an online-learned decorrelation matrix, achieving the best mean AUC (0.8027) among 15 methods on 50 datasets.

  3. A Computational Ethical Framework for Financial Digital Phenotyping for Mental Health

    cs.LO 2026-07 conditional novelty 5.0 of 10

    Ethical rules for financial digital phenotyping can be written as deontic temporal constraints whose violations Z3 proves unsatisfiable inside the formal model.

  4. ClouDens: Operational Context-Aware Anomaly Detection for Large-scale Cloud System Monitoring

    cs.NI 2026-07 conditional novelty 5.0 of 10

    ClouDens detects cloud telemetry anomalies by partitioning metrics into status/aggregation subsets, building context-aware graphs, and forecasting with ST-GNNs, beating a GRU baseline on IBM Cloud data.

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