# Ensemble2: Anomaly Detection via EVT-Ensemble Framework for Seasonal KPIs in Communication Network

arXiv: https://arxiv.org/abs/2205.14305 · HTML: https://mlsanity.com/paper/2205.14305/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 17 references that are in the map.
- First posted on arXiv 28 May 2022.

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

- [AdaRNN: Adaptive Learning and Forecasting of Time Series](https://mlsanity.com/paper/2108.04443/) (2021)
- [Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training](https://mlsanity.com/paper/2102.02887/) (2021)
- [OnlineSTL: Scaling Time Series Decomposition by 100x](https://mlsanity.com/paper/2107.09110/) (2021)

## Abstract (arXiv)

KPI anomaly detection is one important function of network management system. Traditional methods either require prior knowledge or manually set thresholds. To overcome these shortcomings, we propose the Ensemble2 framework, which applies ensemble learning to improve exogenous capabilities. Meanwhile, automatically adjusts thresholds based on extreme value theory. The model is tested on production datasets to verify its effectiveness. We further optimize the model using online learning, and finally running at a speed of ~10 pts/s on an Intel i5 platform.