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Posted on Jul 17, 2024

H5 for A B C D E F G H I J K I M N 0 P 0 R Seasonality -- compute a seasonally

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H5 for A B C D E F G H I J K I M N 0 P 0 R Seasonality -- compute a seasonally adjusted Regression forecast. 1 2 Intercept = 8109,8 3 Slope = 54,00 4 Year 5 6 7 8 9 10 2018 11 12 HINT: =(Previous regression forecast * Seasonal Index for that month) Seasonal Trend Seasonally ABS Historical Annual Ratio (within Adjustment Adjustment Percentage Month Period Sales average year) (Regression) Regression Error Abs(error) error Error Squared January 1 4.302 8.384 0,51 8.164 February 2. 3.338 8.384 0,40 8.218 March 3 7.700 8.384 0,92 8.272 April 4 11.820 8.384 1.41 8.326 May 5 13.898 8.384 1,66 8.380 June 6 11.902 8.384 1,42 8.434 July 7 10.961 8.384 1,31 8.488 August 8 10.863 8.384 1,30 8.542 September 9 7.883 8.384 0.94 8.596 October 10 6.957 8.384 0,83 8.650 November 11 5.820 8.384 0,69 8.704 December 12 5.164 8.384 0,62 8.758 January 13 4.381 9.013 0,49 8.812 February 14 3.677 9.013 0,41 8.866 March 15 8.505 9.013 0,94 8.920 April 16 13.789 9.013 1,53 8.974 May 17 14.366 9.013 1,59 9.028 June 18 12.504 9.013 1,39 9.082 2017 #2 - MA #3 - WMA #4 - SES #5 - Regression #6 - Seaonality Summary + Seasonality Index (all years) January 1 February 2 March 3 April 4 May 5 June 6 July 7 August 8 Septemb 9 October 10 Novembe 11 Decembe 12 0,49 0,41 0,91 1,52 1,60 1,41 1,34 1,28 0,94 0,79 0,71 0,60 13 14 15 16 17 18 19 20 21 22 2019 MA Bereit + 10 A ZUT H I J K L M N 0 P 0 R 19 23 24 20 25 21 26 22 27 23 28 24 25 29 30 26 31 27 B July August September October November December January February March April May June July August September October November December January February D 12.176 11.301 8.557 6.905 6.685 5.306 4.795 4.062 8.646 16.159 15.450 14.079 13.565 12.780 9.116 7.824 6.762 5.919 E 9.013 9.013 9.013 9.013 9.013 9.013 9.930 9.930 9.930 9.930 9.930 9.930 9.930 9.930 9.930 9.930 9.930 9.930 F 1,35 1,25 0,95 0,77 0.74 0.59 0,48 0,41 0.87 1,63 1,56 1,42 1,37 1,29 0.92 0,79 0,68 0,60 32 33 28 G 9.136 9.190 9.244 9.298 9.352 9.406 9.460 9.514 9.568 9.622 9.676 9.730 9.784 9.838 9.892 9.946 10.000 10.054 10.108 10.162 29 30 34 35 2020 31 36 32 37 33 38 34 39 35 40 36 41 37 42 38 Cumulative Sum Forecast Error Mean Absolute Deviation Mean Absolute Percentage Error 43 March Mean Squared Error 39 40 44 April 45 CFE MAD 2021 MAPE MSE May June July 46 41 42 With this method, let's continue 43 into 2021! #3 - WMA #4 - SES #5 - Regression 10.216 10.270 10.324 10.378 10.432 #6 - Seaonality 47 #2 - MA Summary + M A F H I J K L M N 0 P Q R 27 31 28 32 33 29 30 34 35 2020 31 B March April May June July August September October November December January February D 8.646 16.159 15.450 14.079 13.565 12.780 9.116 7.824 6.762 5.919 E 9.930 9.930 9.930 9.930 9.930 9.930 9.930 9.930 9.930 9.930 0,87 1,63 1,56 1,42 1,37 1.29 0,92 0,79 0,68 0,60 36 32 G 9.568 9.622 9.676 9.730 9.784 9.838 9.892 9.946 10.000 10.054 10.108 10.162 37 33 38 34 39 35 40 36 41 37 42 38 Cumulative Sum Forecast Error Mean Absolute Deviation Mean Absolute Percentage Error 43 39 Mean Squared Error 44 40 45 2021 41 CFE MAD MAPE MSE 46 47 March April May June July August September October November December 42 With this method, let's continue into 2021! 44 43 10.216 10.270 10.324 10.378 10.432 10.486 10.540 10.594 10.648 10.702 48 49 45 50 46 Note that we can't compute errors here, as the actual sales haven't happened yet! 51 47 52 48 53 54 #2 - MA #3 - WMA #4 - SES #5 - Regression #6 - Seaonality Summary + H5 for A B C D E F G H I J K I M N 0 P 0 R Seasonality -- compute a seasonally adjusted Regression forecast. 1 2 Intercept = 8109,8 3 Slope = 54,00 4 Year 5 6 7 8 9 10 2018 11 12 HINT: =(Previous regression forecast * Seasonal Index for that month) Seasonal Trend Seasonally ABS Historical Annual Ratio (within Adjustment Adjustment Percentage Month Period Sales average year) (Regression) Regression Error Abs(error) error Error Squared January 1 4.302 8.384 0,51 8.164 February 2. 3.338 8.384 0,40 8.218 March 3 7.700 8.384 0,92 8.272 April 4 11.820 8.384 1.41 8.326 May 5 13.898 8.384 1,66 8.380 June 6 11.902 8.384 1,42 8.434 July 7 10.961 8.384 1,31 8.488 August 8 10.863 8.384 1,30 8.542 September 9 7.883 8.384 0.94 8.596 October 10 6.957 8.384 0,83 8.650 November 11 5.820 8.384 0,69 8.704 December 12 5.164 8.384 0,62 8.758 January 13 4.381 9.013 0,49 8.812 February 14 3.677 9.013 0,41 8.866 March 15 8.505 9.013 0,94 8.920 April 16 13.789 9.013 1,53 8.974 May 17 14.366 9.013 1,59 9.028 June 18 12.504 9.013 1,39 9.082 2017 #2 - MA #3 - WMA #4 - SES #5 - Regression #6 - Seaonality Summary + Seasonality Index (all years) January 1 February 2 March 3 April 4 May 5 June 6 July 7 August 8 Septemb 9 October 10 Novembe 11 Decembe 12 0,49 0,41 0,91 1,52 1,60 1,41 1,34 1,28 0,94 0,79 0,71 0,60 13 14 15 16 17 18 19 20 21 22 2019 MA Bereit + 10 A ZUT H I J K L M N 0 P 0 R 19 23 24 20 25 21 26 22 27 23 28 24 25 29 30 26 31 27 B July August September October November December January February March April May June July August September October November December January February D 12.176 11.301 8.557 6.905 6.685 5.306 4.795 4.062 8.646 16.159 15.450 14.079 13.565 12.780 9.116 7.824 6.762 5.919 E 9.013 9.013 9.013 9.013 9.013 9.013 9.930 9.930 9.930 9.930 9.930 9.930 9.930 9.930 9.930 9.930 9.930 9.930 F 1,35 1,25 0,95 0,77 0.74 0.59 0,48 0,41 0.87 1,63 1,56 1,42 1,37 1,29 0.92 0,79 0,68 0,60 32 33 28 G 9.136 9.190 9.244 9.298 9.352 9.406 9.460 9.514 9.568 9.622 9.676 9.730 9.784 9.838 9.892 9.946 10.000 10.054 10.108 10.162 29 30 34 35 2020 31 36 32 37 33 38 34 39 35 40 36 41 37 42 38 Cumulative Sum Forecast Error Mean Absolute Deviation Mean Absolute Percentage Error 43 March Mean Squared Error 39 40 44 April 45 CFE MAD 2021 MAPE MSE May June July 46 41 42 With this method, let's continue 43 into 2021! #3 - WMA #4 - SES #5 - Regression 10.216 10.270 10.324 10.378 10.432 #6 - Seaonality 47 #2 - MA Summary + M A F H I J K L M N 0 P Q R 27 31 28 32 33 29 30 34 35 2020 31 B March April May June July August September October November December January February D 8.646 16.159 15.450 14.079 13.565 12.780 9.116 7.824 6.762 5.919 E 9.930 9.930 9.930 9.930 9.930 9.930 9.930 9.930 9.930 9.930 0,87 1,63 1,56 1,42 1,37 1.29 0,92 0,79 0,68 0,60 36 32 G 9.568 9.622 9.676 9.730 9.784 9.838 9.892 9.946 10.000 10.054 10.108 10.162 37 33 38 34 39 35 40 36 41 37 42 38 Cumulative Sum Forecast Error Mean Absolute Deviation Mean Absolute Percentage Error 43 39 Mean Squared Error 44 40 45 2021 41 CFE MAD MAPE MSE 46 47 March April May June July August September October November December 42 With this method, let's continue into 2021! 44 43 10.216 10.270 10.324 10.378 10.432 10.486 10.540 10.594 10.648 10.702 48 49 45 50 46 Note that we can't compute errors here, as the actual sales haven't happened yet! 51 47 52 48 53 54 #2 - MA #3 - WMA #4 - SES #5 - Regression #6 - Seaonality Summary +