# A Comparative Study on Unsupervised Anomaly Detection for Time Series: Experiments and Analysis

arXiv: https://arxiv.org/abs/2209.04635 · HTML: https://mlsanity.com/paper/2209.04635/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 50 references that are in the map.
- First posted on arXiv 10 Sep 2022.

## Builds on (most cited first, 9 of 9)

- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding](https://mlsanity.com/paper/1802.04431/) (2018)
- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection](https://mlsanity.com/paper/1607.00148/) (2016)
- [Deep Learning for Anomaly Detection: A Review](https://mlsanity.com/paper/2007.02500/) (2020)
- [Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmark](https://mlsanity.com/paper/1510.03336/) (2015)
- [Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series](https://mlsanity.com/paper/2010.05073/) (2020)
- [Anomaly Detection in Multivariate Non-stationary Time Series for Automatic DBMS Diagnosis](https://mlsanity.com/paper/1708.02635/) (2017)
- [HIFI: Anomaly Detection for Multivariate Time Series with High-order Feature Interactions](https://mlsanity.com/paper/2106.06167/) (2021)

## Built on by (most cited first, 11 of 11)

- [Towards a General Time Series Anomaly Detector with Adaptive Bottlenecks and Dual Adversarial Decoders](https://mlsanity.com/paper/2405.15273/) (2024)
- [Nominality Score Conditioned Time Series Anomaly Detection by Point/Sequential Reconstruction](https://mlsanity.com/paper/2310.15416/) (2023)
- [CrossAD: Time Series Anomaly Detection with Cross-scale Associations and Cross-window Modeling](https://mlsanity.com/paper/2510.12489/) (2025)
- [Sub-Adjacent Transformer: Improving Time Series Anomaly Detection with Reconstruction Error from Sub-Adjacent Neighborhoods](https://mlsanity.com/paper/2404.18948/) (2024)
- [Anomaly Detection and Generation with Diffusion Models: A Survey](https://mlsanity.com/paper/2506.09368/) (2025)
- [Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter](https://mlsanity.com/paper/2311.04190/) (2023)
- [Data Quality Monitoring for the Hadron Calorimeters Using Transfer Learning for Anomaly Detection](https://mlsanity.com/paper/2408.16612/) (2024)
- [LLM-Assisted Logic Rule Learning: Scaling Human Expertise for Time Series Anomaly Detection](https://mlsanity.com/paper/2601.19255/) (2026)
- [Moon: A Modality Conversion-based Efficient Multivariate Time Series Anomaly Detection](https://mlsanity.com/paper/2510.01970/) (2025)
- [SCAN: Enhance Time Series Anomaly Detection via Multi-Scale Neighborhood-Centered Clustering](https://mlsanity.com/paper/2606.19255/) (2026)
- [Detecting the Undetectable: Enhancing Unsupervised time series Anomaly Detection via Active Learning](https://mlsanity.com/paper/2607.00720/) (2026)

## Abstract (arXiv)

The continued digitization of societal processes translates into a proliferation of time series data that cover applications such as fraud detection, intrusion detection, and energy management, where anomaly detection is often essential to enable reliability and safety. Many recent studies target anomaly detection for time series data. Indeed, area of time series anomaly detection is characterized by diverse data, methods, and evaluation strategies, and comparisons in existing studies consider only part of this diversity, which makes it difficult to select the best method for a particular problem setting. To address this shortcoming, we introduce taxonomies for data, methods, and evaluation strategies, provide a comprehensive overview of unsupervised time series anomaly detection using the taxonomies, and systematically evaluate and compare state-of-the-art traditional as well as deep learning techniques. In the empirical study using nine publicly available datasets, we apply the most commonly-used performance evaluation metrics to typical methods under a fair implementation standard. Based on the structuring offered by the taxonomies, we report on empirical studies and provide guidelines, in the form of comparative tables, for choosing the methods most suitable for particular application settings. Finally, we propose research directions for this dynamic field.