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A Survey of Deep Anomaly Detection in Multivariate Time Series: Taxonomy, Applications, and Directions

Computer Science

A Survey of Deep Anomaly Detection in Multivariate Time Series: Taxonomy, Applications, and Directions

F. Wang, Y. Jiang, et al.

This paper reviews recent deep learning techniques for multivariate time series anomaly detection, proposing a taxonomy of detection strategies, summarizing advantages and drawbacks, and organizing public datasets and application domains. It highlights challenges in modeling temporal dependencies and inter-variable relationships. This research was conducted by Authors present in <Authors> tag.

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~3 min • Beginner • English
Citation Metrics
Citations
11
Influential Citations
1
Reference Count
116
Citation by Year

Note: The citation metrics presented here have been sourced from Semantic Scholar and OpenAlex.

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