# Is Your Anomaly Detector Ready for Change? Adapting AIOps Solutions to the Real World

arXiv: https://arxiv.org/abs/2311.10421 · HTML: https://mlsanity.com/paper/2311.10421/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 43 references that are in the map.
- First posted on arXiv 17 Nov 2023.

## Builds on (most cited first, 7 of 7)

- [Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications](https://mlsanity.com/paper/1802.03903/) (2018)
- [Time-Series Anomaly Detection Service at Microsoft](https://mlsanity.com/paper/1906.03821/) (2019)
- [Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress](https://mlsanity.com/paper/2009.13807/) (2020)
- [From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectors](https://mlsanity.com/paper/2203.11070/) (2022)
- [Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Process](https://mlsanity.com/paper/2110.10234/) (2021)
- [Towards a consistent interpretation of AIOps models](https://mlsanity.com/paper/2202.02298/) (2022)
- [A Systematic Mapping Study in AIOps](https://mlsanity.com/paper/2012.09108/) (2020)

## Built on by (most cited first, 3 of 3)

- [An Efficient Model Maintenance Approach for MLOps](https://mlsanity.com/paper/2412.04657/) (2024)
- [Sustainable Machine Learning Retraining: Optimizing Energy Efficiency Without Compromising Accuracy](https://mlsanity.com/paper/2506.13838/) (2025)
- [Prepared for the Unknown: Adapting AIOps Capacity Forecasting Models to Data Changes](https://mlsanity.com/paper/2510.10320/) (2025)

## Abstract (arXiv)

Anomaly detection techniques are essential in automating the monitoring of IT systems and operations. These techniques imply that machine learning algorithms are trained on operational data corresponding to a specific period of time and that they are continuously evaluated on newly emerging data. Operational data is constantly changing over time, which affects the performance of deployed anomaly detection models. Therefore, continuous model maintenance is required to preserve the performance of anomaly detectors over time. In this work, we analyze two different anomaly detection model maintenance techniques in terms of the model update frequency, namely blind model retraining and informed model retraining. We further investigate the effects of updating the model by retraining it on all the available data (full-history approach) and only the newest data (sliding window approach). Moreover, we investigate whether a data change monitoring tool is capable of determining when the anomaly detection model needs to be updated through retraining.