AI models evaluated for network utilization forecasting

A new research paper evaluates various AI and machine learning models for forecasting network utilization KPIs. The study compares traditional methods like seasonal decomposition and Prophet against machine learning algorithms such as Random Forest, XGBoost, and Support Vector Regression, as well as deep learning architectures like Convolutional LSTMs. The models were benchmarked using common metrics including MAPE, NRMSE, and R-square to provide insights into the trade-offs between accuracy and computational efficiency for network capacity planning. AI

IMPACT Provides insights for selecting optimal AI models for network capacity planning and resource provisioning.

RANK_REASON The item is an academic paper detailing research into AI/ML models for time series forecasting. [lever_c_demoted from research: ic=1 ai=1.0]

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