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Fig. 2. Training data formed by aggregate data samples (a) and final configuration of the training data, arranged to be used by the models (b). Fig. 2.b shows the training data ready to be finally used by the models. It can be seen that the data is arranged in small "elementary" flows of 3 samples (corresponding to an elapsed time of 3 seconds). These elementary flows form small vector time-series which are the data entry for training and prediction. The flows are obtained according to a sliding window of width 3 and offset 1 applied to the data in Fig. 2.a. The offset causes the successive flows to have one overlapping sample. For each elementary flow, the models will be trained with QoE errors for the current time-step and the next time-step. In this way, at prediction time, we will be able to detect which errors are occurring in the current time-step and predict errors in the next time interval. Following the arrangement shown in Fig. 2.b, we finally obtain 2078 elementary flows, which we then divide into 1766 training flows and 312 test flows (15% of total flows). These will be the final data sets that will be used to train and validate all models presented in this Doctoral Thesis: Novel applications of Machine Learning to NTAP - 152PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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