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computational demand for this task. Acknowledgments: We would like to express our gratitude for the support and data provided by Telefonica M2M, who is actively working in the application of machine learning techniques to optimize their business. The data used were originally obfuscated with no access to raw data at any time. Besides, this work has been partially funded by the Ministerio de Economía y Competitividad del Gobierno de España and the Fondo de Desarrollo Regional (FEDER) within the project "Inteligencia distribuida para el control y adaptación de redes dinámicas definidas por software, Ref: TIN2014-57991-C3-2-P", in the Programa Estatal de Fomento de la Investigación Científica y Técnica de Excelencia, Subprograma Estatal de Generación de Conocimiento. References [1] Leo Breiman, Random Forests. Machine Learning, Volume 45, Issue 1, October 1 2001, Pages 5-32 [2] Jerome H. Friedman, Greedy Function Approximation: A Gradient Boosting Machine, The Annals of Statistics, Vol. 29, No. 5 (Oct., 2001), pp. 1189-1232 [3] Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning: Data mining, inference, and prediction. Second Edition (2009), Springer Series in Statistics, Pages 219-230 [4] Georg Heinze and Michael Schemper, A solution to the problem of separation in logistic regression, Statistics in Medicine, Volume 21, Issue 16 (30 August 2002), Pages 2409–2419. [5] Andrew Gelman et al, A weakly informative default prior distribution for logistic and other regression models, The Annals of Applied Statistics 2008, Vol. 2, No. 4, Pages 1360– 1383 [6] Lawrence R. Rabiner, A tutorial on Hidden Markov Models and selected applications in speech recognition. Proceedings of the IEEE, Vol. 77, No. 2. (06 February 1989), Pages. 257- 286 [7] Charles C Holt, Forecasting Trends and Seasonal by Exponentially Weighted Averages. International Journal of Forecasting, Volume 20, Issue 1 (January–March 2004), Pages 5–10. [8] M. Xie et al, A seasonal ARIMA model with exogenous variables for Elspot electricity prices in Sweden, 2013 10th International Conference on the European Energy Market (EEM), Stockholm (May 2013), Pages 1-4 [9] Cristina Stolojescu-Crisan, Data mining based wireless network traffic forecasting, 2012 10th International Symposium on Electronics and Telecommunications (ISETC), Timisoara (Nov 2012), Pages 115-118. [10] Josef Kittler et al, On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 3 (March 1998), Pages 226-239. [11] Papadopouli M, Evaluation of short-term traffic forecasting algorithms in wireless networks, 2006 2nd Conference on Next Generation Internet Design and Engineering, NGI, Valencia (April 2006), Pages 102-109 [12] Yantai Shu et al, Wireless traffic modeling and prediction using seasonal ARIMA models, IEEE International Conference on Communications, 2003 (ICC 2003), Anchorage Doctoral Thesis: Novel applications of Machine Learning to NTAP - 109PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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