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CPS1834 Gumgum D. et al.
                Based on the MSE value for P = 3 the hijri calendar is better than  the
            Gregorian calendar represented by the SARIMA (1,0,1) model (1,0,1) 12, while
            for  P  =  6  and  12  the  Christian  calendar  is  better  than  the  hijri  calendar
            represented by the same model.
                Based on the MAPE value for P = 3 the hijri calendar is better than the
            Gregorian calendar represented by the SARIMA (1,0,1) model (1,0,1) 12, while
            for  P  =  6  and  12  the  Gregorian  calendar  is  better  than  the  hijri  calendar
            represented by the same model.Based on the AIC Value for all periods the
            Gregorian Calendar is better than the Hijri calendar, for P = 3 and P = 12
            represented by the SARIMA (1,0,1) (1,0,1) 12 model while for P = 6 represented
            by the SARIMA model (1,0,1) (1,0,2) 12.
                From the results of tracking in the previous step, the best model for the
            two data above is selected. The two best models are the SARIMA (1,0,1) (1,0,1)
            12 and SARIMA (1,0,1) (1,0,2) models 12.
                Comparisons are made based on the smallest values of the four criteria
            above (MSE, MAD, MAPE and AIC). The smaller the value of the criteria, the
            better the model is obtained. The chosen horizon is P = 3.6 and 12 according
            to the length of the period.

            5.  Discussion and conclusion
                Both of these data (Gregorian and Hijri) have the same pattern and period,
            namely, seasonal patterns and periods of 12.SARIMA modeling based on the
            Gregorian Calendar is better than SARIMA based on the Hijri Calendar for
            forecasting at 12 and 6 future observations. SARIMA modeling based on the
            Hijri Calendar is better than SARIMA based on the Gregorian Calendar for
            forecasting on the 3 future observations.

            References
            1.  C.Sivapragasam,shie-yui liong and m.f.k.Pasha (2001),” Rainfall and
                 Runoff forecasting with SSA-SVM approach”,Journal of
                 Hydroinformatics,141-152.
            2.  Asce,upsm and Jothiprakash,V (2015)” Extraction of Nonlinear Rainfall
                 Trends Using Singular Spectrum Analysis”, Journal of Hydrologic
                 Engineering,20(12):05015007.
            3.  C.L Wu and K.W.Chau (2011),”Rainfall-ruoff modelling using artificial
                 neural network coupled with singular spectrum analysis”,Journal of
                 Hyidrology,399,394-409.
            4.  D.Baratta,G Cicion,F.Masulli and L.Studer (2003),”Application of an
                 ensemble technique based on singular spectrum analysis to daily rainfall
                 forecasting”,Neural Networks,16,375-387.




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