Reconstruction-Based Methods for Multivariate Time Series Anomaly Detection : A Review and Taxonomy
Year: 2025
Type: conference-paper
Abstract: Reconstruction-based methods have become a central paradigm for unsupervised multivariate time series anomaly detection (MTSAD), especially in the context of cyber-physical and industrial control systems. Their ability to learn normal patterns and detect deviations without supervision makes them highly suitable for safety-critical environments. In this work, we conduct a structured review of recent deep learning models for MTSAD based on reconstruction principles. We propose a four-axis taxonomy that captures key design dimensions: (i) backbone architecture (e.g., AE, VAE, GAN, Transformer); (ii) temporal/frequency modeling; (iii) hybridization strategies; and (iv) integration of attention mechanisms. We analyze representative models including MCA-VAE, TFMAE, ALGAN, DiffGAN, and CAE-Transformer, discussing their loss functions, anomaly scoring strategies, interpretability, and thresholding mechanisms. We also highlight critical challenges such as overfitting to anomalies, fixed thresholds under distribution shift, and the difficulty of modeling both temporal and inter-variable dependencies. This survey aims to clarify terminology, compare architectural families, and identify trends and research gaps. We provide a structured comparative table and discuss the implications for real-world deployment.
Institution: Centre d'Etudes Superieures Industrielles
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4415348553
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