Classification of Anomalies in Telecommunication Network KPI Time Series
Korantin Bordeau-Aubert Justin Whatley Sylvain Nadeau Tristan Glatard Brigitte Jaumard
The increasing complexity and scale of telecommunication networks have led to
a growing interest in automated anomaly detection systems. However, the
classification of anomalies detected on network Key Performance Indicators
(KPI) has received less attention, resulting in a lack of information about
anomaly characteristics and classification processes. To address this gap, this
paper proposes a modular anomaly classification framework. The framework
assumes separate entities for the anomaly classifier and the detector, allowing
for a distinct treatment of anomaly detection and classification tasks on time
series. The objectives of this study are (1) to develop a time series simulator
that generates synthetic time series resembling real-world network KPI
behavior, (2) to build a detection model to identify anomalies in the time
series, (3) to build classification models that accurately categorize detected
anomalies into predefined classes (4) to evaluate the classification framework
performance on simulated and real-world network KPI time series. This study has
demonstrated the good performance of the anomaly classification models trained
on simulated anomalies when applied to real-world network time series data.