Summary of Dive Into Time-series Anomaly Detection: a Decade Review, by Paul Boniol et al.
Dive into Time-Series Anomaly Detection: A Decade Review
by Paul Boniol, Qinghua Liu, Mingyi Huang, Themis Palpanas, John Paparrizos
First submitted to arxiv on: 29 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Databases (cs.DB); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a comprehensive survey on time-series anomaly detection, grouping existing solutions under a process-centric taxonomy. It highlights the need for machine learning-based approaches to tackle the growing volume and velocity of streaming data. The authors provide an original categorization of anomaly detection methods and perform a meta-analysis of the literature, outlining general trends in time-series anomaly detection research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Time series analytics is crucial in today’s world with increasing amounts of data being collected. Anomaly detection is a key part of this process, helping us identify unusual patterns in fields like cybersecurity, finance, law enforcement, and healthcare. While there are many machine learning algorithms out there, the paper helps make sense of them by grouping existing solutions into categories. It also looks at the big picture, highlighting what’s working well and what areas need more attention. |
Keywords
» Artificial intelligence » Anomaly detection » Attention » Machine learning » Time series
