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

arXiv: https://arxiv.org/abs/2507.07559 · HTML: https://mlsanity.com/paper/2507.07559/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 59 references that are in the map.
- First posted on arXiv 10 Jul 2025.

## Builds on (most cited first, 17 of 17)

- [Deep Semi-Supervised Anomaly Detection](https://mlsanity.com/paper/1906.02694/) (2019)
- [Deep Anomaly Detection Using Geometric Transformations](https://mlsanity.com/paper/1805.10917/) (2018)
- [ADBench: Anomaly Detection Benchmark](https://mlsanity.com/paper/2206.09426/) (2022)
- [PyOD: A Python Toolbox for Scalable Outlier Detection](https://mlsanity.com/paper/1901.01588/) (2019)
- [Extended Isolation Forest](https://mlsanity.com/paper/1811.02141/) (2018)
- [Detecting semantic anomalies](https://mlsanity.com/paper/1908.04388/) (2019)
- [Benchmarking Unsupervised Outlier Detection with Realistic Synthetic Data](https://mlsanity.com/paper/2004.06947/) (2020)
- [Dive into Time-Series Anomaly Detection: A Decade Review](https://mlsanity.com/paper/2412.20512/) (2024)
- [Unsupervised anomaly detection algorithms on real-world data: how many do we need?](https://mlsanity.com/paper/2305.00735/) (2023)
- [Evaluating Latent Space Robustness and Uncertainty of EEG-ML Models under Realistic Distribution Shifts](https://mlsanity.com/paper/2209.11233/) (2022)
- [PIDForest: Anomaly Detection via Partial Identification](https://mlsanity.com/paper/1912.03582/) (2019)
- [Towards Communication-efficient and Attack-Resistant Federated Edge Learning for Industrial Internet of Things](https://mlsanity.com/paper/2012.04436/) (2020)
- [Constrained Parameter Inference as a Principle for Learning](https://mlsanity.com/paper/2203.13203/) (2022)
- [Correlations Are Ruining Your Gradient Descent](https://mlsanity.com/paper/2407.10780/) (2024)
- [Temporal anomaly detection: calibrating the surprise](https://mlsanity.com/paper/1705.10085/) (2017)
- [Real Time Anomaly Detection And Categorisation](https://mlsanity.com/paper/2009.06670/) (2020)
- [Active Learning of SVDD Hyperparameter Values](https://mlsanity.com/paper/1912.01927/) (2019)

## Abstract (arXiv)

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.