# Time Series Anomaly Detection via Reinforcement Learning-Based Model Selection

arXiv: https://arxiv.org/abs/2205.09884 · HTML: https://mlsanity.com/paper/2205.09884/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 45 references that are in the map.
- First posted on arXiv 19 May 2022.

## Builds on (most cited first, 11 of 11)

- [A Unifying Review of Deep and Shallow Anomaly Detection](https://mlsanity.com/paper/2009.11732/) (2020)
- [Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding](https://mlsanity.com/paper/1802.04431/) (2018)
- [PyOD: A Python Toolbox for Scalable Outlier Detection](https://mlsanity.com/paper/1901.01588/) (2019)
- [ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions](https://mlsanity.com/paper/2201.00382/) (2022)
- [COPOD: Copula-Based Outlier Detection](https://mlsanity.com/paper/2009.09463/) (2020)
- [Deep Nearest Neighbor Anomaly Detection](https://mlsanity.com/paper/2002.10445/) (2020)
- [Time Series Anomaly Detection for Smart Grids: A Survey](https://mlsanity.com/paper/2107.08835/) (2021)
- [ModelLight: Model-Based Meta-Reinforcement Learning for Traffic Signal Control](https://mlsanity.com/paper/2111.08067/) (2021)
- [Reinforcement Learning based dynamic weighing of Ensemble Models for Time Series Forecasting](https://mlsanity.com/paper/2008.08878/) (2020)
- [Automatic Hyperparameter Tuning Method for Local Outlier Factor, with Applications to Anomaly Detection](https://mlsanity.com/paper/1902.00567/) (2019)
- [Reinforcement Learning based Dynamic Model Selection for Short-Term Load Forecasting](https://mlsanity.com/paper/1811.01846/) (2018)

## Built on by (most cited first, 4 of 4)

- [Unsupervised Model Selection for Time-series Anomaly Detection](https://mlsanity.com/paper/2210.01078/) (2022)
- [mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scale](https://mlsanity.com/paper/2506.21550/) (2025)
- [RAMSeS: Robust and Adaptive Model Selection for Time-Series Anomaly Detection Algorithms](https://mlsanity.com/paper/2602.21766/) (2026)
- [Self-Adaptive Anomaly Detection with Reinforcement Learning and Human Feedback in Connected Vehicles](https://mlsanity.com/paper/2607.08373/) (2026)

## Abstract (arXiv)

Time series anomaly detection has been recognized as of critical importance for the reliable and efficient operation of real-world systems. Many anomaly detection methods have been developed based on various assumptions on anomaly characteristics. However, due to the complex nature of real-world data, different anomalies within a time series usually have diverse profiles supporting different anomaly assumptions. This makes it difficult to find a single anomaly detector that can consistently outperform other models. In this work, to harness the benefits of different base models, we propose a reinforcement learning-based model selection framework. Specifically, we first learn a pool of different anomaly detection models, and then utilize reinforcement learning to dynamically select a candidate model from these base models. Experiments on real-world data have demonstrated that the proposed strategy can indeed outplay all baseline models in terms of overall performance.