# Automated Model Selection for Time-Series Anomaly Detection

arXiv: https://arxiv.org/abs/2009.04395 · HTML: https://mlsanity.com/paper/2009.04395/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2020.

## Built on by (most cited first, 9 of 9)

- [Unsupervised Model Selection for Time-series Anomaly Detection](https://mlsanity.com/paper/2210.01078/) (2022)
- [TimeRecipe: A Time-Series Forecasting Recipe via Benchmarking Module Level Effectiveness](https://mlsanity.com/paper/2506.06482/) (2025)
- [mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scale](https://mlsanity.com/paper/2506.21550/) (2025)
- [Towards Robust Multi-Modal Reasoning via Model Selection](https://mlsanity.com/paper/2310.08446/) (2023)
- [Ranked from Within: Ranking Large Multimodal Models Without Labels](https://mlsanity.com/paper/2412.06461/) (2024)
- [MSAD: A Deep Dive into Model Selection for Time series Anomaly Detection](https://mlsanity.com/paper/2510.26643/) (2025)
- [RAMSeS: Robust and Adaptive Model Selection for Time-Series Anomaly Detection Algorithms](https://mlsanity.com/paper/2602.21766/) (2026)
- [Refining the Optimization Target for Automatic Univariate Time Series Anomaly Detection in Monitoring Services](https://mlsanity.com/paper/2307.10653/) (2023)
- [Reasoning-based Anomaly Detection Framework: A Real-time, Scalable, and Automated Approach to Anomaly Detection Across Domains](https://mlsanity.com/paper/2510.03486/) (2025)

## Abstract (arXiv)

Time-series anomaly detection is a popular topic in both academia and industrial fields. Many companies need to monitor thousands of temporal signals for their applications and services and require instant feedback and alerts for potential incidents in time. The task is challenging because of the complex characteristics of time-series, which are messy, stochastic, and often without proper labels. This prohibits training supervised models because of lack of labels and a single model hardly fits different time series. In this paper, we propose a solution to address these issues. We present an automated model selection framework to automatically find the most suitable detection model with proper parameters for the incoming data. The model selection layer is extensible as it can be updated without too much effort when a new detector is available to the service. Finally, we incorporate a customized tuning algorithm to flexibly filter anomalies to meet customers' criteria. Experiments on real-world datasets show the effectiveness of our solution.