Análisis de series de tiempo con métodos econométricos para el control de congestión en redes de telecomunicaciones
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Palabras clave

control
congestión
redes de comunicaciones
modelos económicos
mercado bursátil

Cómo citar

Sánchez cifuentes, J., & Cuellar Chaves, M. (2018). Análisis de series de tiempo con métodos econométricos para el control de congestión en redes de telecomunicaciones. Plataforma Abierta De Libros Y Memorias Académicas - PALMA, 96. Recuperado a partir de https://cipres.sanmateo.edu.co/ojs/index.php/libros/article/view/344

Resumen

A continuación, se relata las ideas que se desarrollan en el transcurso de este texto, en donde el propósito general es generar modelos de control de congestión en redes de comunicaciones a partir de modelos económicos aplicados al comportamiento del mercado bursátil.
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Citas

Abed, G. A., Ismail, M., & Jumari, K. (2012). Exploration and evaluation of traditional TCP congestion control techniques. Journal of King Saud University-Computer and Information Sciences, 24(2), 145–155.

Allison, P. D. (1999). Multiple regression: A primer. Pine Forge Press.

Amandi, A. (2001). Desarrollo de sistemas Multiagentes. Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial, 5(13), 33–35.

Anja Feldmann Anna C. Gilbert Polly Huang, W. W. (1999). Dynamics of IP traffic: A study of the role of variability and the impact of control. SIGCOMM ’99. R. B. ans M. Shafiee and A. Dadlani, “Adaptive generalized minimum variance congestion controller for dynamic tcp/aqm networks,”

Atiya, A. F., El-Shoura, S. M., Shaheen, S. I., & El-Sherif, M. S. (1999). A comparison between neural-network forecasting techniques-case study: river flow forecasting. IEEE Transactions on Neural Networks, 10(2), 402–409.

Azadeh, A., & Tarverdian, S. (2007). Integration of genetic algorithm, computer simulation and design of experiments for forecasting electrical energy consumption. Energy Policy, 35(10), 5229–5241.

Blumberg, D. F. (2004). Introduction to management of reverse logistics and closed loop supply chain processes. CRC Press.

Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.

Buckley, J. J. (2005). Fuzzy statistics: hypothesis testing. Soft Computing, 9(7), 512–518.

Calyam P.; Krymskiy, D. . S. M. . S. P. (2005). Active and passive measurements on campus, regional and national network backbone paths. Computer Communications and Networks, 2005. ICCCN 2005. Proceedings. 14th International Conference on, 1, 537–542.

Campero, F. J. R. (n.d.). Introducción a la programación en Netlogo.

Chan, Y.-C., Lin, C.-L., Chan, C.-T., & Ho, C.-Y. (2008). Research, Innovation and Vision for the Future, 2008. RIVF 2008. IEEE International Conference on. Ind. Eng. Chem. Res., 33, 1013–1029.

Chang, B.-J., Lin, S.-Y., & Jin, J.-Y. (2009). LIAD: Adaptive bandwidth prediction based Logarithmic Increase Adaptive Decrease for TCP congestion control in heterogeneous wireless networks. Computer Networks, 53(14), 2566–2585.

Chen, S.-M. (1996). Forecasting enrollments based on fuzzy time series. Fuzzy Sets and Systems, 81(3), 311–319.

Connor, J. T., Martin, R. D., & Atlas, L. E. (1994). Recurrent neural networks and robust time series prediction. IEEE Transactions on Neural Networks, 5(2), 240–254.

Devore, J. (2007). Making sense of data: A practical guide to exploratory data analysis and data mining. Taylor & Francis.

Diebold, F. X. (1998). Elements of forecasting. Citeseer.

Dina Katabi Mark Handley, C. R. (2002). Congestion Control for High Bandwidth-Delay Product Networks. In SIGCOMM ’02.

Dubova, I. (2005). La validación y aplicabilidad de la teoría de portafolio en el caso colombiano. Cuadernos de Administración, 18(30), 241–279.

Gariboldi, G. (1999). Comercio electrónico: conceptos y reflexiones básicas (Vol. 4). BID-INTAL.

Granger, C. W. J. (2001). Some Properties of Time Series Data and Their Use in Econometric Model Specification* CWJ Granger. Essays in Econometrics: Collected Papers of Clive WJ Granger, 2, 119.

Grimaldi, R. P. (1998). Matemáticas discreta y combinatoria: introducción y aplicaciones. Pearson Educación.

Guan, X., & Chen, C. (n.d.). Adaptive fuzzy control for chaotic systems with H? tracking performance.

Guan, X., & Chen, C. (2005). H Variable universe adaptive fuzzy control for chaotic system, 24, 1075–1086.

Hamzaçebi, C., Akay, D., & Kutay, F. (2009). Comparison of direct and iterative artificial neural network forecast approaches in multi-periodic time series forecasting. Expert Systems with Applications, 36(2), 3839–3844.

Hsu, C., & Wen, Y. (1998). Improved grey prediction models for the trans pacific air passenger market. Transportation Planning and Technology, 22(2), 87–107.

Hylleberg, S. (1992). Modelling seasonality. Oxford University Press.

Jain, R. (n.d.). Congestion Control in Computer Networks: Issues and Trends.

Joshi, R. (2006). Development of fuzzy time series model for agricultural production forecasting. Govind Ballabh Pant University of Agriculture and Technology; Pantnagar.

Kelly, F. (2008). Fairness and stability of end-to-end congestion control. SIGCOMM Computer Communication Review.

Krugman, P. R., Obstfeld, M., & Melitz, M. J. (2012). Economía international: Teoría y política. Pearson.

L. Massoulie, J. W. R. (2000). Bandwidth sharing and admission control for elastic traffic. Telecommunication Systems, 15, 17.

Lai, R. K., Fan, C.-Y., Huang, W.-H., & Chang, P.-C. (2009). Evolving and clustering fuzzy decision tree for financial time series data forecasting. Expert Systems with Applications, 36(2), 3761–3773.

Lazasr, A. A., & Pacific, G. (n.d.). Control of Resources in Broadband Networks with Quality of Service Guarntees.

Lee, C. C., & Ou-Yang, C. (2009). A neural networks approach for forecasting the supplier’s bid prices in supplier selection negotiation process. Expert Systems with Applications, 36(2), 2961–2970.

Leu, Y., Lee, C.-P., & Jou, Y.-Z. (2009). A distance-based fuzzy time series model for exchange rates forecasting. Expert Systems with Applications, 36(4), 8107–8114.

Lewis, D. (2008). Convention: A philosophical study. John Wiley & Sons.

Lin, X., & Shroff, N. B. (2004). On the stability region of congestion control. In Proceedings of the Allerton Conference on Communications, Control and Computing.

Liu, H.-T. (2009). An integrated fuzzy time series forecasting system. Expert Systems with Applications, 36(6), 10045–10053.

Low, S. H., Paganini, F., & Doyle, J. C. (2002). Internet congestion control. Control Systems, IEEE, 22(1), 28–43.

Nino, L. F., Ardila, E., & Sanchez, J. F. (2013). Congestion control model for local IP networks. In Communications and Computing (COLCOM), 2013 IEEE Colombian Conference on (pp. 1–6).

Pareja, I. A. V. (2013). Decisiones de inversión: para la valoración financiera de proyectos y empresas. Pontificia Universidad Javeriana.

Pei, L. J., Mu, X. W., Wang, R. M., & Yang, J. P. (n.d.). Dynamics of the Internet TCP RED congestion control system.

Puri, P., & Kohli, M. (2007). Forecasting student admission in colleges with NEURAL networks. International Journal of Computer Science and Network Security, 7(11), 298–303.

Refenes, A. N., & Azema-Barac, M. (1994). Neural network applications in financial asset management. Neural Computing & Applications, 2(1), 13–39.

Rosberg, Z., Matthews, J., & Zukerman, M. (n.d.). A network rate management protocol with TCP congestion control and fairness for all.

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Derechos de autor 2018 Fundación Universitaria San Mateo

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