Page 100 - ITU Journal - ICT Discoveries - Volume 1, No. 2, December 2018 - Second special issue on Data for Good
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ITU JOURNAL: ICT Discoveries, Vol. 1(2), December 2018





                 Statistic      957 samples      707 samples      178 samples       72 samples    15 samples
                                  (Hourly)          (Daily)        (Weekly)       (Monthly)      (Quarterly)
             Probability          0.000000         0.000000        0.065425        0.010755       0.323506

                                           Table 4 – CANFIS architecture training results

                       CANFIS Architecture                 MSE             NMSE         R         %  Error
                   (Training: Validation: Testing)
             (70%:15%:15%)                               0.00298          0.0303       0.71         32.34
             (70%:10%:20%)                               0.00373          0.0339       0.70         34.89
             (80%:10%:10%)                               0.00460          0.0377       0.698        36.70
             (60%:10%:30%)                                0.0034          0.0328       0.683        36.24

                                      Table 5 – Selection of network architecture for forecasting
                 CANFIS Model         5-3-5          5-3-5          5-2-5            5-2-5          5-2-5


             Transfer function       TanhAxon      TanhAxon        TanhAxon          Axon         TanhAxon
             Learning Rule             Step          RProp          RProp         Momentum          Step
             MSE                      0.0092        0.0021         0.00108         0.000406        0.00559
             NRMSE                    0.0531        0.0252         0.01825          0.01120        0.0415
             R                        0.5968        0.7868          0.9268          0.9737         0.6262
             % Error                  48.37          21.05          17.82            12.33          52.55



          The  CANFIS  (5-2-5)  model  testing  window  with
          convergence rate for the data is shown in Fig. 2. The
          active  cost  curve  plot  indicates  that  with  testing
          data  the  algorithm  has  successfully  undergone
          generalization and thereby converging to zero.

          3.3  Network Validation

          The CANFIS (5-2-5) network for five-input five-
          output data with 2 MFs per input, was validated by            Fig. 2 – CANFIS (5-2-5) testing window
          comparing  the  actual  and  predicted  traffic  as   The study found out that with the increase in the
          demonstrated in Fig. 3 for hourly traffic prediction.   number  of  inputs,  the  CANFIS  model  produce
                                                               accurate  traffic  forecasting  when  membership
          The daily, weekly, monthly and quarterly prediction   functions per input are 2. Therefore the best model
          of 3G traffic are exhibited in Fig. 4, Fig. 5, Fig. 6 and   for predicting five-input telecommunication traffic
          Fig.  7.  The  CANFIS  model  employed  the  Bell    was CANFIS (5-2-5).
          membership  function,  Axon  transfer  function  and
          Momentum learning rule. The membership function
          per input was varied between 2 and 7.



















         78                                  © International Telecommunication Union, 2018
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