# Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly Detection

arXiv: https://arxiv.org/abs/1804.07091 · HTML: https://mlsanity.com/paper/1804.07091/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2018.

## Built on by (most cited first, 18 of 18)

- [Corner Cases for Visual Perception in Automated Driving: Some Guidance on Detection Approaches](https://mlsanity.com/paper/2102.05897/) (2021)
- [Explainable Time Series Anomaly Detection using Masked Latent Generative Modeling](https://mlsanity.com/paper/2311.12550/) (2023)
- [Supervised Anomaly Detection via Conditional Generative Adversarial Network and Ensemble Active Learning](https://mlsanity.com/paper/2104.11952/) (2021)
- [A Survey of Visual Sensory Anomaly Detection](https://mlsanity.com/paper/2202.07006/) (2022)
- [Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time series](https://mlsanity.com/paper/2212.11080/) (2022)
- [Cross-Modal Learning for Anomaly Detection in Complex Industrial Process: Methodology and Benchmark](https://mlsanity.com/paper/2406.09016/) (2024)
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- [PVGRU: Generating Diverse and Relevant Dialogue Responses via Pseudo-Variational Mechanism](https://mlsanity.com/paper/2212.09086/) (2022)
- [Anomaly Attribution of Multivariate Time Series using Counterfactual Reasoning](https://mlsanity.com/paper/2109.06562/) (2021)
- [Computing Divergences between Discrete Decomposable Models](https://mlsanity.com/paper/2112.04583/) (2021)
- [A Survey on Time-Series Distance Measures](https://mlsanity.com/paper/2412.20574/) (2024)
- [Cluster-Wide Task Slowdown Detection in Cloud System](https://mlsanity.com/paper/2408.04236/) (2024)
- [Fraud Analytics: A Decade of Research -- Organizing Challenges and Solutions in the Field](https://mlsanity.com/paper/2212.04329/) (2022)
- [Unraveling Anomalies in Time: Unsupervised Discovery and Isolation of Anomalous Behavior in Bio-regenerative Life Support System Telemetry](https://mlsanity.com/paper/2406.09825/) (2024)
- [RRCNN: A novel signal decomposition approach based on recurrent residue convolutional neural network](https://mlsanity.com/paper/2307.01725/) (2023)
- [Computing Marginal and Conditional Divergences between Decomposable Models with Applications](https://mlsanity.com/paper/2310.09129/) (2023)
- [Anomalous Agreement: How to find the Ideal Number of Anomaly Classes in Correlated, Multivariate Time Series Data](https://mlsanity.com/paper/2501.07172/) (2025)
- [Advancing climate model interpretability: Feature attribution for Arctic melt anomalies](https://mlsanity.com/paper/2502.07741/) (2025)

## Abstract (arXiv)

Automatic detection of anomalies in space- and time-varying measurements is an important tool in several fields, e.g., fraud detection, climate analysis, or healthcare monitoring. We present an algorithm for detecting anomalous regions in multivariate spatio-temporal time-series, which allows for spotting the interesting parts in large amounts of data, including video and text data. In opposition to existing techniques for detecting isolated anomalous data points, we propose the "Maximally Divergent Intervals" (MDI) framework for unsupervised detection of coherent spatial regions and time intervals characterized by a high Kullback-Leibler divergence compared with all other data given. In this regard, we define an unbiased Kullback-Leibler divergence that allows for ranking regions of different size and show how to enable the algorithm to run on large-scale data sets in reasonable time using an interval proposal technique. Experiments on both synthetic and real data from various domains, such as climate analysis, video surveillance, and text forensics, demonstrate that our method is widely applicable and a valuable tool for finding interesting events in different types of data.