ReviewSensors (Basel, Switzerland)2025
A Survey of Deep Anomaly Detection in Multivariate Time Series: Taxonomy, Applications, and Directions.
Review in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed.
- Nested Spatiotemporal Anomaly Detection with Semantic Augmentation: A Case Study in Heritage Conservation.Sensors (Basel, Switzerland) · 2026Article
- Fusion of Embedded Vision and Intelligent Algorithms for Non-Contact Deformation Monitoring.Sensors (Basel, Switzerland) · 2026Article
- Batch Distillation Data for Developing Machine Learning Anomaly Detection Methods.Scientific data · 2026Article
- DML-LLM Hybrid Architecture for Fault Detection and Diagnosis in Sensor-Rich Industrial Systems.Sensors (Basel, Switzerland) · 2026Article
- Article
- Convolutional autoencoder based condition monitoring system for unique complex technical systems.Scientific reports · 2025Article
- A Comparative Study of Lightweight, Sparse Autoencoder-Based Classifiers for Edge Network Devices: An Efficiency Analysis of Feed-Forward and Deep Neural Networks.Sensors (Basel, Switzerland) · 2025Article
- FedSW-TSAD: SWGAN-Based Federated Time Series Anomaly Detection.Sensors (Basel, Switzerland) · 2025Article
- An Unsupervised Fusion Strategy for Anomaly Detection via Chebyshev Graph Convolution and a Modified Adversarial Network.Biomimetics (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Multivariate time series anomaly detection (MTSAD) can effectively identify and analyze anomalous behavior in complex systems, which is particularly important in fields such as financial monitoring, industrial equipment fault detection, and cybersecurity. MTSAD requires simultaneously analyze temporal dependencies and inter-variable relationships have prompted researchers to develop specialized deep learning models to detect anomalous patterns. In this paper, we conducted a structured and comprehensive overview of the latest techniques in deep learning for multivariate time series anomaly detection methods. Firstly, we proposed a taxonomy for the anomaly detection strategies from the perspectives of learning paradigms and deep learning models, and then provide a systematic review that emphasizes their advantages and drawbacks. We also organized the public datasets for time series anomaly detection along with their respective application domains. Finally, open issues for future research on MTSAD were identified.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.