Summary of Online Model-based Anomaly Detection in Multivariate Time Series: Taxonomy, Survey, Research Challenges and Future Directions, by Lucas Correia et al.
Online Model-based Anomaly Detection in Multivariate Time Series: Taxonomy, Survey, Research Challenges and Future Directions
by Lucas Correia, Jan-Christoph Goos, Philipp Klein, Thomas Bäck, Anna V. Kononova
First submitted to arxiv on: 7 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposed survey provides an extensive overview of state-of-the-art model-based online semi- and unsupervised anomaly detection approaches for multivariate time-series data. The taxonomy introduced distinguishes between online and offline, as well as training and inference, while presenting popular datasets and evaluation metrics used in the literature. Furthermore, it categorizes models into different families based on their properties. However, the biggest research challenge revolves around benchmarking, with a lack of reliable comparison methods due to flawed public datasets and inadequate evaluation metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Time-series anomaly detection is important for engineering processes like development, manufacturing, and other operations involving dynamic systems. The goal of this survey is to provide an overview of state-of-the-art approaches for detecting anomalies in multivariate time-series data. It introduces a new way of grouping models based on their properties and discusses popular datasets and evaluation metrics used in the field. However, the biggest challenge is finding ways to compare different approaches fairly, which is currently a major problem. |
Keywords
» Artificial intelligence » Anomaly detection » Inference » Time series » Unsupervised
