Predição de Vazão de Rede com Imputação de Dados baseada em Modelos de Regressão

• Maria L. Linhares UECE
• Janaina R. Santos UECE
• Ismael F. de Castro UECE
• Maria C. Mesquita UECE
• Alex F. R. Trajano UECE / IA
• Rafael L. Gomes UECE

DOI: https://doi.org/10.5753/wgrs.2026.23091

Resumo

Medições periódicas fornecem métricas essenciais para monitoramento do desempenho da rede, tais como vazão, perda e atraso. Contudo, essas medições são suscetíveis a falhas que resultam em dados ausentes, dificultando ações de gerenciamento primordiais, como a predição do desempenho. Dentro deste contexto, este trabalho apresenta uma abordagem para predição de vazão de rede com imputação de dados baseada em modelos de regressão. A proposta realiza a imputação dos valores faltantes utilizando um modelo Ensemble Stacking Regressor e, em seguida, modelos de aprendizado de máquina são usados para a predição do desempenho. Em experimentos utilizando dados reais da Rede Nacional de Pesquisa (RNP), os resultados mostram que uma imputação de alta qualidade melhora substancialmente a precisão das predições.

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