# Dive into Time-Series Anomaly Detection: A Decade Review

arXiv: https://arxiv.org/abs/2412.20512 · HTML: https://mlsanity.com/paper/2412.20512/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2024.

## Built on by (most cited first, 24 of 24)

- [Time-RA: Towards Time Series Reasoning for Anomaly Diagnosis with LLM Feedback](https://mlsanity.com/paper/2507.15066/) (2025)
- [mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scale](https://mlsanity.com/paper/2506.21550/) (2025)
- [PaAno: Patch-Based Representation Learning for Time-Series Anomaly Detection](https://mlsanity.com/paper/2602.01359/) (2026)
- [Building Machine Learning Challenges for Anomaly Detection in Science](https://mlsanity.com/paper/2503.02112/) (2025)
- [Adaptive Conformal Anomaly Detection with Time Series Foundation Models for Signal Monitoring](https://mlsanity.com/paper/2604.20122/) (2026)
- [MSAD: A Deep Dive into Model Selection for Time series Anomaly Detection](https://mlsanity.com/paper/2510.26643/) (2025)
- [Unsupervised Feature Construction for Anomaly Detection in Time Series -- An Evaluation](https://mlsanity.com/paper/2501.07999/) (2025)
- [CoCAI: Copula-based Conformal Anomaly Identification for Multivariate Time-Series](https://mlsanity.com/paper/2507.17796/) (2025)
- [AnoF-Diff: One-Step Diffusion-Based Anomaly Detection for Forceful Tool Use](https://mlsanity.com/paper/2509.15153/) (2025)
- [Disjoint or Overlapping? Inference Windowing for Reconstruction-Based Time Series Anomaly Detection](https://mlsanity.com/paper/2606.09874/) (2026)
- [Real-Time Decorrelation-Based Anomaly Detection for Multivariate Time Series](https://mlsanity.com/paper/2507.07559/) (2025)
- [A Comprehensive Forecasting-Based Framework for Time Series Anomaly Detection: Benchmarking on the Numenta Anomaly Benchmark (NAB)](https://mlsanity.com/paper/2510.11141/) (2025)
- [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)
- [GNNs for Time Series Anomaly Detection: An Open-Source Framework and a Critical Evaluation](https://mlsanity.com/paper/2603.09675/) (2026)
- [EngineAD: A Real-World Vehicle Engine Anomaly Detection Dataset](https://mlsanity.com/paper/2603.25955/) (2026)
- [ClouDens: Operational Context-Aware Anomaly Detection for Large-scale Cloud System Monitoring](https://mlsanity.com/paper/2607.18127/) (2026)
- [TimeRLM: Recursive Language Models Enable Precise Anomaly Localization in Long-Context Time-Series](https://mlsanity.com/paper/2608.03391/) (2026)
- [PRISM: Powerful Time Series to Image (TS2I) Representations for Multivariate Anomaly Detection](https://mlsanity.com/paper/2608.03926/) (2026)
- [Continuous Online Fault Detection for Mobile Robots via Adaptive Edge Models](https://mlsanity.com/paper/2609.29194/) (2026)
- [In a Streaming World, Should You Stand Still? A Comprehensive Benchmark of Anomaly Detection in Streams](https://mlsanity.com/paper/2609.39215/) (2026)
- [What Streaming Anomaly Detection Finds (and Misses) in Industrial Time Series](https://mlsanity.com/paper/2609.39232/) (2026)
- [From Benchmarks to Production: Transferring Time Series Anomaly Detection Methods for Electricity Production Monitoring](https://mlsanity.com/paper/2609.39257/) (2026)
- [Detect, Explain, Interpret: An End-to-End Benchmark for Time Series Anomaly Detection, Explainability and Interpretability](https://mlsanity.com/paper/2610.01168/) (2026)
- [Retrieval Over Training: Similarity search-based Model Selection for Time Series Anomaly Detection](https://mlsanity.com/paper/2610.20243/) (2026)

## Abstract (arXiv)

Recent advances in data collection technology, accompanied by the ever-rising volume and velocity of streaming data, underscore the vital need for time series analytics. In this regard, time-series anomaly detection has been an important activity, entailing various applications in fields such as cyber security, financial markets, law enforcement, and health care. While traditional literature on anomaly detection is centered on statistical measures, the increasing number of machine learning algorithms in recent years call for a structured, general characterization of the research methods for time-series anomaly detection. This survey groups and summarizes anomaly detection existing solutions under a process-centric taxonomy in the time series context. In addition to giving an original categorization of anomaly detection methods, we also perform a meta-analysis of the literature and outline general trends in time-series anomaly detection research.