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time-series methods. In Figure , the final format used is shown, with columns having: SIM identification (ID), date/hour, several numeric indices which provide information about day of the week, hour of the day account number, access point and rate plan. The data is presented in sequential time-ordered along rows grouped by SIM. The data in Figure corresponds to a block of data for a particular SIM ordered by time, following this data block we have a consecutive data block for other SIM ordered in the same way and so on. This data arrangement allows to apply all the methods under study. Figure 2. Transformed data format used for all methods. The processed dataset contains circa 4 million entries, each one indicating whether data has been transmitted by a given SIM in a given hour or not. 3. Results 3.1 Results from non-time-series methods In Figure 3, we present the results for the non-time-series methods and in following paragraphs the details about each method. The best results are obtained for random forest (see Figure 8). The upper chart in Figure 3 gives the mean accuracy of prediction (for all SIMs) in periods of one-hour for a prediction interval of 48 hours. The bottom chart gives the standard deviation (amount of variation) of the mean prediction accuracy (please note the opposite nature of both charts, as a higher accuracy is associated with a lower standard deviation). It is interesting the periodic nature of the forecasting performance. A sharp reduction in performance, as the prediction time increases, would have been more expected, but the results Doctoral Thesis: Novel applications of Machine Learning to NTAP - 99PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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