Imputação Adaptativa em Espaço Latente para Séries Temporais de Vazão de Rede
Resumo
O monitoramento de vazão é essencial para a Qualidade de Serviço (QoS), mas falhas de medição geram lacunas que comprometem a previsão de desempenho da rede. Dentro deste contexto, este trabalho propõe um algoritmo que reconstrói séries temporais combinando matrizes de Hankel e a imputação em espaço latente. Resultados experimentais, com dados reais, indicam que a proposta preserva a dinâmica temporal da rede superiormente aos métodos tradicionais, reduzindo erros de imputação e aumentando a confiabilidade de modelos preditivos como GRU e LTSM.Referências
Adedayo, A. O. and Twala, B. (2017). Qos functionality in software defined network. In 2017 International Conference on Information and Communication Technology Convergence (ICTC), pages 693–699.
Alaya, B., Khan, R., Moulahi, T., and el Khediri, S. (2021). Study on qos management for video streaming in vehicular ad hoc network (vanet). Wireless Personal Communications, 118:1–33.
Alkenani, J. and Nassar, K. (2022). Network monitoring measurements for quality of service: A review. Iraqi Journal for Electrical and Electronic Engineering, 18:33–42.
Arquam, M. and Kumari, S. (2025). Maximization of Communication Network Throughput Using Dynamic Traffic Allocation Scheme, page 116–127. Springer Nature Singapore.
Brito, M. L. L., Ferreira, M. C. M., Portela, A. L. C., and Gomes, R. L. (2025). Ai-based estimation of bandwidth availability for data offloading in edge-cloud computing. IEEE Networking Letters, pages 1–1.
Ferreira, M. C., Ribeiro, S. E., Nobre, F. V., Linhares, M. L., Araújo, T. P., and Gomes, R. L. (2024). Mitigating measurement failures in throughput performance forecasting. In 2024 20th International Conference on Network and Service Management (CNSM), pages 1–7.
Ghahramani, M. H., Zhou, M., and Hon, C. T. (2017). Toward cloud computing qos architecture: analysis of cloud systems and cloud services. IEEE/CAA Journal of Automatica Sinica, 4(1):6–18.
Gomes, R. L., Bittencourt, L. F., and Madeira, E. R. (2014). A bandwidth-feasibility algorithm for reliable virtual network allocation. In 2014 IEEE 28th International Conference on Advanced Information Networking and Applications, pages 504–511.
Gomes, R. L., Bittencourt, L. F., Madeira, E. R., Cerqueira, E., and Gerla, M. (2016). Bandwidth-aware allocation of resilient virtual software defined networks. Computer Networks, 100:179–194.
Ju, H. and Zhang, R. (2014). Throughput maximization in wireless powered communication networks. IEEE Transactions on Wireless Communications, 13(1):418–428.
Khattab, A. A. R., Elshennawy, N. M., and Fahmy, M. (2023). Gma: Gap imputing algorithm for time series missing values. Journal of Electrical Systems and Information Technology, 10(1):41.
Khayati, M., Lerner, A., Tymchenko, Z., and Cudré-Mauroux, P. (2020). Mind the gap: an experimental evaluation of imputation of missing values techniques in time series. Proc. VLDB Endow., 13(5):768–782.
Liu, X., Xu, B., Zheng, K., and Zheng, H. (2023). Throughput maximization of wireless-powered communication network with mobile access points. IEEE Transactions on Wireless Communications, 22(7):4401–4415.
Liu, X., Yu, Y., Li, F., and Durrani, T. S. (2022). Throughput maximization for ris-uav relaying communications. IEEE Transactions on Intelligent Transportation Systems, 23(10):19569–19574.
Lopes Gomes, R. and Roberto Mauro Madeira, E. (2012). A traffic classification agent for virtual networks based on qos classes. IEEE Latin America Transactions, 10(3):1734–1741.
Minovski, D., Ögren, N., Mitra, K., and Åhlund, C. (2023). Throughput prediction using machine learning in lte and 5g networks. IEEE Transactions on Mobile Computing, 22(3):1825–1840.
Niako, N., Melgarejo, J. D., Maestre, G. E., and Vatcheva, K. P. (2024). Effects of missing data imputation methods on univariate blood pressure time series data analysis and forecasting with arima and lstm. BMC Medical Research Methodology, 24(1):320.
Nobre, F. V. J., Silva, D. d. S., Ferreira, M. C. M. M., Brito, M. L. M. L., de Araújo, T. P., and Gomes, R. L. (2025). Time-weighted correlation approach to identify high delay links in internet service providers. Journal of Internet Services and Applications, 16(1):419–430.
Sharma, A., Pandit, S., and Talluri, S. R. (2025). Throughput prediction of fifth-generation cellular system using hybrid feature selection and enhanced sequential decision tree machine learning algorithm. Wireless Networks, 31(3):3025–3042.
Souza, M. S., Ribeiro, S. E. S. B., Lima, V. C., Cardoso, F. J., and Gomes, R. L. (2024). Combining regular expressions and machine learning for sql injection detection in urban computing. Journal of Internet Services and Applications, 15(1):103–111.
Younas, M. I., Iqbal, M. J., Aziz, A., and Sodhro, A. H. (2023). Toward qos monitoring in iot edge devices driven healthcare—a systematic literature review. Sensors, 23(21).
Zheng, Z., Wu, X., Zhang, Y., Lyu, M. R., and Wang, J. (2013). Qos ranking prediction for cloud services. IEEE Transactions on Parallel and Distributed Systems, 24(6):1213–1222.
Alaya, B., Khan, R., Moulahi, T., and el Khediri, S. (2021). Study on qos management for video streaming in vehicular ad hoc network (vanet). Wireless Personal Communications, 118:1–33.
Alkenani, J. and Nassar, K. (2022). Network monitoring measurements for quality of service: A review. Iraqi Journal for Electrical and Electronic Engineering, 18:33–42.
Arquam, M. and Kumari, S. (2025). Maximization of Communication Network Throughput Using Dynamic Traffic Allocation Scheme, page 116–127. Springer Nature Singapore.
Brito, M. L. L., Ferreira, M. C. M., Portela, A. L. C., and Gomes, R. L. (2025). Ai-based estimation of bandwidth availability for data offloading in edge-cloud computing. IEEE Networking Letters, pages 1–1.
Ferreira, M. C., Ribeiro, S. E., Nobre, F. V., Linhares, M. L., Araújo, T. P., and Gomes, R. L. (2024). Mitigating measurement failures in throughput performance forecasting. In 2024 20th International Conference on Network and Service Management (CNSM), pages 1–7.
Ghahramani, M. H., Zhou, M., and Hon, C. T. (2017). Toward cloud computing qos architecture: analysis of cloud systems and cloud services. IEEE/CAA Journal of Automatica Sinica, 4(1):6–18.
Gomes, R. L., Bittencourt, L. F., and Madeira, E. R. (2014). A bandwidth-feasibility algorithm for reliable virtual network allocation. In 2014 IEEE 28th International Conference on Advanced Information Networking and Applications, pages 504–511.
Gomes, R. L., Bittencourt, L. F., Madeira, E. R., Cerqueira, E., and Gerla, M. (2016). Bandwidth-aware allocation of resilient virtual software defined networks. Computer Networks, 100:179–194.
Ju, H. and Zhang, R. (2014). Throughput maximization in wireless powered communication networks. IEEE Transactions on Wireless Communications, 13(1):418–428.
Khattab, A. A. R., Elshennawy, N. M., and Fahmy, M. (2023). Gma: Gap imputing algorithm for time series missing values. Journal of Electrical Systems and Information Technology, 10(1):41.
Khayati, M., Lerner, A., Tymchenko, Z., and Cudré-Mauroux, P. (2020). Mind the gap: an experimental evaluation of imputation of missing values techniques in time series. Proc. VLDB Endow., 13(5):768–782.
Liu, X., Xu, B., Zheng, K., and Zheng, H. (2023). Throughput maximization of wireless-powered communication network with mobile access points. IEEE Transactions on Wireless Communications, 22(7):4401–4415.
Liu, X., Yu, Y., Li, F., and Durrani, T. S. (2022). Throughput maximization for ris-uav relaying communications. IEEE Transactions on Intelligent Transportation Systems, 23(10):19569–19574.
Lopes Gomes, R. and Roberto Mauro Madeira, E. (2012). A traffic classification agent for virtual networks based on qos classes. IEEE Latin America Transactions, 10(3):1734–1741.
Minovski, D., Ögren, N., Mitra, K., and Åhlund, C. (2023). Throughput prediction using machine learning in lte and 5g networks. IEEE Transactions on Mobile Computing, 22(3):1825–1840.
Niako, N., Melgarejo, J. D., Maestre, G. E., and Vatcheva, K. P. (2024). Effects of missing data imputation methods on univariate blood pressure time series data analysis and forecasting with arima and lstm. BMC Medical Research Methodology, 24(1):320.
Nobre, F. V. J., Silva, D. d. S., Ferreira, M. C. M. M., Brito, M. L. M. L., de Araújo, T. P., and Gomes, R. L. (2025). Time-weighted correlation approach to identify high delay links in internet service providers. Journal of Internet Services and Applications, 16(1):419–430.
Sharma, A., Pandit, S., and Talluri, S. R. (2025). Throughput prediction of fifth-generation cellular system using hybrid feature selection and enhanced sequential decision tree machine learning algorithm. Wireless Networks, 31(3):3025–3042.
Souza, M. S., Ribeiro, S. E. S. B., Lima, V. C., Cardoso, F. J., and Gomes, R. L. (2024). Combining regular expressions and machine learning for sql injection detection in urban computing. Journal of Internet Services and Applications, 15(1):103–111.
Younas, M. I., Iqbal, M. J., Aziz, A., and Sodhro, A. H. (2023). Toward qos monitoring in iot edge devices driven healthcare—a systematic literature review. Sensors, 23(21).
Zheng, Z., Wu, X., Zhang, Y., Lyu, M. R., and Wang, J. (2013). Qos ranking prediction for cloud services. IEEE Transactions on Parallel and Distributed Systems, 24(6):1213–1222.
Publicado
25/05/2026
Como Citar
FERREIRA, Maria C. M. M.; CASTRO, Ismael S. F. de; LINHARES, Maria de L.; GOMES, Rafael L..
Imputação Adaptativa em Espaço Latente para Séries Temporais de Vazão de Rede. In: WORKSHOP DE GERÊNCIA E OPERAÇÃO DE REDES E SERVIÇOS (WGRS), 31. , 2026, Praia do Forte/BA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 141-154.
ISSN 2595-2722.
DOI: https://doi.org/10.5753/wgrs.2026.24068.
