# Adaptive Two-Stage Online Learning for Service-Affecting Failure Detection in Mobile Core Networks

arXiv: https://arxiv.org/abs/2607.18522 · HTML: https://mlsanity.com/paper/2607.18522/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 20 Jul 2026.

## Builds on (most cited first, 3 of 3)

- [Time-Series Anomaly Detection Service at Microsoft](https://mlsanity.com/paper/1906.03821/) (2019)
- [River: machine learning for streaming data in Python](https://mlsanity.com/paper/2012.04740/) (2020)
- [Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment](https://mlsanity.com/paper/1510.04026/) (2015)

## Abstract (arXiv)

Mobile network operators monitor aggregated traffic volumes to assess the operational health of core network infrastructure. Reliable failure detection is challenging due to strong temporal structure, non-stationarity, measurement artefacts, and extreme class imbalance, which limit static threshold-based monitoring. This paper proposes a two-stage online learning framework for traffic-based failure detection in mobile core networks. Stage I incrementally models normal traffic dynamics using lightweight regression with time-aware features. Stage II analyses prediction residuals together with contextual indicators to detect genuine service-affecting network failures. The framework operates fully online under a prequential evaluation protocol, enabling continuous adaptation with low computational overhead. Across linear and non-linear models, the proposed two-stage architecture achieves the best precision-recall trade-off, attaining the highest recall, F1-score, and AUC at acceptable false positive rates. These results demonstrate the importance of explicit residual decomposition for reliable failure detection in streaming mobile core network data.