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

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.... show more
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Introduction
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Discussion
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Limitations
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