Unified Taxonomy for Multivariate Time Series Anomaly Detection using Deep Learning

This paper presents a comprehensive classification system (taxonomy) for organizing deep learning methods used to detect anomalies in multivariate time series data. The researchers identified eleven key dimensions across three categories (Input, Output, and Model) by analyzing existing research and found that the field is increasingly moving toward Transformer-based models and approaches that either reconstruct or predict data patterns.