Classification of Anomalies in Telecommunication Network KPI Time Series

TL;DR

This paper introduces a modular framework for classifying anomalies in telecommunication network KPI time series, including a simulator, detection, and classification models, validated on both synthetic and real data.

Contribution

It presents a novel modular approach separating detection and classification, along with a synthetic data generator for improved anomaly classification in network KPIs.

Findings

Classification models trained on simulated data perform well on real data

The framework effectively distinguishes different anomaly types

Synthetic data generation aids in model training and evaluation

Abstract

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.