Abstract:
This study addresses the forecasting of Key Performance Indicators (KPIs) in
LTE net-works through a comparative analysis of advanced machine learning
and statistical models, specifically the Gated Recurrent Unit (GRU) and Seasonal
Auto-Regressive Integrated Moving Average (SARIMA) models. Using hourly
data from a mobile network operator, the analysis identifies and leverages
temporal and statistical patterns, including seasonality and trends, within the
KPI dataset to enhance model training
