Real-Time Decorrelation-Based Anomaly Detection for Multivariate Time Series

2025/07/10 by Amirhossein Sadough, Mahyar Shahsavari, Sadough, Amirhossein +5
Computer Science · #Anomaly Detection Techniques and Applications #Data Stream Mining Techniques #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Signal Processing (eess.SP) #Systems and Control (eess.SY) #Time Series Analysis and Forecasting #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2507.07559

openalex publication_date 2025/07/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Anomaly detection (AD) plays a vital role across a wide range of real-world domains by identifying data instances that deviate from expected patterns, potentially signaling critical events such as system failures, fraudulent activities, or rare medical conditions. The demand for real-time AD has surged with the rise of the (Industrial) Internet of Things, where massive volumes of multivariate sensor data must be processed instantaneously. Real-time AD requires methods that not only handle high-dimensional streaming data but also operate in a single-pass manner, without the burden of storing historical instances, thereby ensuring minimal memory usage and fast decision-making. We propose DAD, a novel real-time decorrelation-based anomaly detection method for multivariate time series, based on an online decorrelation learning approach. Unlike traditional proximity-based or reconstruction-based detectors that process entire data or windowed instances, DAD dynamically learns and monitors the correlation structure of data sample by sample in a single pass, enabling efficient and effective detection. To support more realistic benchmarking practices, we also introduce a practical hyperparameter tuning strategy tailored for real-time anomaly detection scenarios. Extensive experiments on widely used benchmark datasets demonstrate that DAD achieves the most consistent and superior performance across diverse anomaly types compared to state-of-the-art methods. Crucially, its robustness to increasing dimensionality makes it particularly well-suited for real-time, high-dimensional data streams. Ultimately, DAD not only strikes an optimal balance between detection efficacy and computational efficiency but also sets a new standard for real-time, memory-constrained anomaly detection.

Citations

• Dive into Time-Series Anomaly Detection: A Decade Review
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• Active Learning of SVDD Hyperparameter Values
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• Deep Semi-Supervised Anomaly Detection
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• Deep Anomaly Detection Using Geometric Transformations
• Unsupervised real-time anomaly detection for streaming data
• A Fast Algorithm for the Minimum Covariance Determinant Estimator
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Related

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• Transformer-based Multivariate Time Series Anomaly Localization
• MIXAD: Memory-Induced Explainable Time Series Anomaly Detection
• Online Multivariate Anomaly Detection and Localization for High-dimensional Settings