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3.2 Results from time-series methods The time-series methods considered have been: HMM, Exponential Smoothing (ETS), ARIMA and ARIMAX. In Figure 6, we present the results for the time-series methods and, in following sections, the details about each method. The upper and bottom charts in the figure give similar information to Figure 3, being the discussion on results also similar. In this case, the best results are obtained for ARIMAX (see Figure 8). The training speed is similar for ARIMA and ETS, and much slower for HMM and ARIMAX, being ARIMAX particularly slow. Training speed for all time-series methods has been worse than for non-time series methods (see section 3.3). In fact, this difference in computing requirements and training speed should be taken into consideration when choosing the final method in a production environment. Figure 6. Performance results for time-series methods. The first time-series method we tried was a HMM [6]. We performed a training of the algorithm for each particular SIM, using as predicted variable the on/off activity during the predicted period. Training an HMM implies defining its four parameters (number of states, observation values and transition and emission probabilities) providing an observation sequence as training data. In our case the observations have two values (on/off SIM activity). We used two hidden states; we observed that increasing the number of hidden states did not improve the results. To obtain the transition and emission probabilities we applied the Baum–Welch algorithm [6] which is a special case of the expectation maximization algorithm. Though HMM usually has good prediction performance for this kind of problems, its results Doctoral Thesis: Novel applications of Machine Learning to NTAP - 103PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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