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• Based in cross-sectional data: o Logistic Regression o Bayesian Logistic Regression o Random Forest o GBM • Based in time-series data: o Exponential Smoothing o HMM o ARIMA o ARIMAX We have additionally explored the combination of results from various classifiers to assess a possible improvement in results. 7.1.4 Results/Conclusions We have obtained a global accuracy over 90% for most of the methods. ARIMAX has provided the highest prediction accuracy, with a global mean accuracy over 93%. Other algorithms as Random Forest, ARIMA and Logistic Regression have provided very good results as well. ARIMAX training time is extremely high, which makes it unsuitable for industrial applications despite its good prediction results. We have observed that connectivity behavior has more structure and less noise than was initially predicted, and that prediction accuracy has a periodic nature over forecasting time. Prediction accuracy gets reduced with forecasting time, but more slowly than expected. Doctoral Thesis: Novel applications of Machine Learning to NTAP - 68PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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