# Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Art

arXiv: https://arxiv.org/abs/2004.00433 · HTML: https://mlsanity.com/paper/2004.00433/

## Key facts

- Cited by 56 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2020.

## Built on by (most cited first, 30 of 56)

- [Deep Time Series Models: A Comprehensive Survey and Benchmark](https://mlsanity.com/paper/2407.13278/) (2024)
- [Deep Learning for Time Series Anomaly Detection: A Survey](https://mlsanity.com/paper/2211.05244/) (2022)
- [Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion](https://mlsanity.com/paper/2202.04770/) (2022)
- [Causal Inference for Time series Analysis: Problems, Methods and Evaluation](https://mlsanity.com/paper/2102.05829/) (2021)
- [Navigating the Metric Maze: A Taxonomy of Evaluation Metrics for Anomaly Detection in Time Series](https://mlsanity.com/paper/2303.01272/) (2023)
- [MST-GAT: A Multimodal Spatial-Temporal Graph Attention Network for Time Series Anomaly Detection](https://mlsanity.com/paper/2310.11169/) (2023)
- [TiSAT: Time Series Anomaly Transformer](https://mlsanity.com/paper/2203.05167/) (2022)
- [AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges](https://mlsanity.com/paper/2304.04661/) (2023)
- [Online false discovery rate control for anomaly detection in time series](https://mlsanity.com/paper/2112.03196/) (2021)
- [Time Series Anomaly Detection using Diffusion-based Models](https://mlsanity.com/paper/2311.01452/) (2023)
- [Unsupervised Deep Learning for IoT Time Series](https://mlsanity.com/paper/2302.03284/) (2023)
- [Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time series](https://mlsanity.com/paper/2212.11080/) (2022)
- [Robust and Explainable Detector of Time Series Anomaly via Augmenting Multiclass Pseudo-Anomalies](https://mlsanity.com/paper/2505.20765/) (2025)
- [mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scale](https://mlsanity.com/paper/2506.21550/) (2025)
- [MOSPAT: AutoML based Model Selection and Parameter Tuning for Time Series Anomaly Detection](https://mlsanity.com/paper/2205.11755/) (2022)
- [Time Series Analysis and Modeling to Forecast: a Survey](https://mlsanity.com/paper/2104.00164/) (2021)
- [A Meta-level Analysis of Online Anomaly Detectors](https://mlsanity.com/paper/2209.05899/) (2022)
- [Towards Total Online Unsupervised Anomaly Detection and Localization in Industrial Vision](https://mlsanity.com/paper/2305.15652/) (2023)
- [Contrastive predictive coding for Anomaly Detection in Multi-variate Time Series Data](https://mlsanity.com/paper/2202.03639/) (2022)
- [Stacked Residuals of Dynamic Layers for Time Series Anomaly Detection](https://mlsanity.com/paper/2202.12457/) (2022)
- [A Review on Self-Supervised Learning for Time Series Anomaly Detection: Recent Advances and Open Challenges](https://mlsanity.com/paper/2501.15196/) (2025)
- [Outlier detection in multivariate functional data through a contaminated mixture model](https://mlsanity.com/paper/2106.07222/) (2021)
- [Time Series Anomaly Detection with label-free Model Selection](https://mlsanity.com/paper/2106.07473/) (2021)
- [Out-of-Distribution Detection in Time-Series Domain: A Novel Seasonal Ratio Scoring Approach](https://mlsanity.com/paper/2207.04306/) (2022)
- [Machine Learning-based vs Deep Learning-based Anomaly Detection in Multivariate Time Series for Spacecraft Attitude Sensors](https://mlsanity.com/paper/2409.17841/) (2024)
- [M2AD: Multi-Sensor Multi-System Anomaly Detection through Global Scoring and Calibrated Thresholding](https://mlsanity.com/paper/2504.15225/) (2025)
- [Improved Anomaly Detection by Using the Attention-Based Isolation Forest](https://mlsanity.com/paper/2210.02558/) (2022)
- [SoftED: Metrics for Soft Evaluation of Time Series Event Detection](https://mlsanity.com/paper/2304.00439/) (2023)
- [Detection of Anomalies in Multivariate Time Series Using Ensemble Techniques](https://mlsanity.com/paper/2308.03171/) (2023)
- [Representation Learning of Limit Order Book: A Comprehensive Study and Benchmarking](https://mlsanity.com/paper/2505.02139/) (2025)

## Abstract (arXiv)

Anomaly detection for time-series data has been an important research field for a long time. Seminal work on anomaly detection methods has been focussing on statistical approaches. In recent years an increasing number of machine learning algorithms have been developed to detect anomalies on time-series. Subsequently, researchers tried to improve these techniques using (deep) neural networks. In the light of the increasing number of anomaly detection methods, the body of research lacks a broad comparative evaluation of statistical, machine learning and deep learning methods. This paper studies 20 univariate anomaly detection methods from the all three categories. The evaluation is conducted on publicly available datasets, which serve as benchmarks for time-series anomaly detection. By analyzing the accuracy of each method as well as the computation time of the algorithms, we provide a thorough insight about the performance of these anomaly detection approaches, alongside some general notion of which method is suited for a certain type of data.