# On the Nature and Types of Anomalies: A Review of Deviations in Data

arXiv: https://arxiv.org/abs/2007.15634 · HTML: https://mlsanity.com/paper/2007.15634/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2020.

## Built on by (most cited first, 22 of 22)

- [Unsupervised anomaly detection algorithms on real-world data: how many do we need?](https://mlsanity.com/paper/2305.00735/) (2023)
- [Online false discovery rate control for anomaly detection in time series](https://mlsanity.com/paper/2112.03196/) (2021)
- [Meta-survey on outlier and anomaly detection](https://mlsanity.com/paper/2312.07101/) (2023)
- [A Hybrid Deep Learning Anomaly Detection Framework for Intrusion Detection](https://mlsanity.com/paper/2212.00966/) (2022)
- [Capture the Flag: Uncovering Data Insights with Large Language Models](https://mlsanity.com/paper/2312.13876/) (2023)
- [We Need to Rethink Benchmarking in Anomaly Detection](https://mlsanity.com/paper/2507.15584/) (2025)
- [How to Use Graph Data in the Wild to Help Graph Anomaly Detection?](https://mlsanity.com/paper/2506.04190/) (2025)
- [Intrinsic Anomaly Detection for Multi-Variate Time Series](https://mlsanity.com/paper/2206.14342/) (2022)
- [Few-shot Weakly-supervised Cybersecurity Anomaly Detection](https://mlsanity.com/paper/2304.07470/) (2023)
- [FoMo X: Modular Explainability Signals for Outlier Detection Foundation Models](https://mlsanity.com/paper/2603.17570/) (2026)
- [GADformer: A Transparent Transformer Model for Group Anomaly Detection on Trajectories](https://mlsanity.com/paper/2303.09841/) (2023)
- [Interpretable Transformation and Analysis of Timelines through Learning via Surprisability](https://mlsanity.com/paper/2503.04502/) (2025)
- [Active Learning-based Isolation Forest (ALIF): Enhancing Anomaly Detection in Decision Support Systems](https://mlsanity.com/paper/2207.03934/) (2022)
- [Classification of Anomalies in Telecommunication Network KPI Time Series](https://mlsanity.com/paper/2308.16279/) (2023)
- [Anomaly Detection in Power Generation Plants with Generative Adversarial Networks](https://mlsanity.com/paper/2310.00335/) (2023)
- [A Data Mining-Based Dynamical Anomaly Detection Method for Integrating with an Advance Metering System](https://mlsanity.com/paper/2405.02574/) (2024)
- [Research information in the light of artificial intelligence: quality and data ecologies](https://mlsanity.com/paper/2405.12997/) (2024)
- [Explainable Deep Convolutional Multi-Type Anomaly Detection](https://mlsanity.com/paper/2511.11165/) (2025)
- [A Trainable Centrality Framework for Modern Data](https://mlsanity.com/paper/2511.22959/) (2025)
- [A New Framework for Explainable Rare Cell Identification in Single-Cell Transcriptomics Data](https://mlsanity.com/paper/2601.01358/) (2026)
- [Time-Series Anomaly Classification for Launch Vehicle Propulsion Systems: Fast Statistical Detectors Enhancing LSTM Accuracy and Data Quality](https://mlsanity.com/paper/2601.06186/) (2026)
- [ProtoX-AD: Self-Explainable Time Series Anomaly Detection and Characterization](https://mlsanity.com/paper/2606.13277/) (2026)

## Abstract (arXiv)

Anomalies are occurrences in a dataset that are in some way unusual and do not fit the general patterns. The concept of the anomaly is typically ill-defined and perceived as vague and domain-dependent. Moreover, despite some 250 years of publications on the topic, no comprehensive and concrete overviews of the different types of anomalies have hitherto been published. By means of an extensive literature review this study therefore offers the first theoretically principled and domain-independent typology of data anomalies and presents a full overview of anomaly types and subtypes. To concretely define the concept of the anomaly and its different manifestations, the typology employs five dimensions: data type, cardinality of relationship, anomaly level, data structure, and data distribution. These fundamental and data-centric dimensions naturally yield 3 broad groups, 9 basic types, and 63 subtypes of anomalies. The typology facilitates the evaluation of the functional capabilities of anomaly detection algorithms, contributes to explainable data science, and provides insights into relevant topics such as local versus global anomalies.