LAMPIRAN. Langkah-Langkah Penggunaan Program Minitab: nama kolom tepat diantara C1 dan angka penjualan pertama Jakarta Muscat

Transkripsi

1 L1 LAMPIRAN Langkah-Langkah Penggunaan Program Minitab: 1. Aktifakan program minitab kemudian copy yang diinginkan pada kolom C1. Beri nama kolom tepat diantara C1 dan angka penjualan pertama Jakarta Muscat Enter. Setelah itu save file di folder yang diinginkan dengan format Jakarta Muscat.MTW 2. Save file

2 L2 3. Uji Korelasi lakukan identifikasi data untuk melihat pola data apakah perlu dilakukan proses differencing dengan melihat hasil autokorelasi nya. Langkahnya sebagai berikut Stat Time series Autocorrelation

3 L3 Kemudian pilih: Di dapatkan Hasil autokorelasi yaitu

4 L4 Tidak ada proses differensiasi jadi nilai tengah ARIMA 0 1. Proses ARIMA (1,0,0)

5 L5

6 L6

7 L7 ARIMA Model: Jakarta-Muscat Estimates at each iteration Iteration SSE Parameters Relative change in each estimate less than Final Estimates of Parameters Type Coef SE Coef T P AR Constant Mean Number of observations: 13 Residuals: SS = (backforecasts excluded) MS = DF = 11 Modified Box-Pierce (Ljung-Box) Chi-Square statistic Lag

8 L8 Chi-Square 15.1 * * * DF 10 * * * P-Value * * * Forecasts from period 13 95% Limits Period Forecast Lower Upper Actual Proses ARIMA (0,0,1)

9 L9

10 L10

11 L11 ARIMA Model: Jakarta-Muscat Estimates at each iteration Iteration SSE Parameters Relative change in each estimate less than Final Estimates of Parameters Type Coef SE Coef T P MA Constant Mean Number of observations: 13 Residuals: SS = (backforecasts excluded) MS = DF = 11 Modified Box-Pierce (Ljung-Box) Chi-Square statistic Lag Chi-Square 14.9 * * * DF 10 * * * P-Value * * *

12 L12 Forecasts from period 13 95% Limits Period Forecast Lower Upper Actual ARIMA (1,0,1)

13 L13

14 L14 ARIMA Model: Jakarta-Muscat Estimates at each iteration Iteration SSE Parameters Relative change in each estimate less than Final Estimates of Parameters Type Coef SE Coef T P AR MA Constant Mean Number of observations: 13 Residuals: SS = (backforecasts excluded) MS = DF = 10

15 L15 Modified Box-Pierce (Ljung-Box) Chi-Square statistic Lag Chi-Square 14.1 * * * DF 9 * * * P-Value * * * Forecasts from period 13 95% Limits Period Forecast Lower Upper Actual ARIMA (2,0,0)

16 L16

17 L17 ARIMA Model: Jakarta-Muscat Estimates at each iteration

18 L18 Iteration SSE Parameters Relative change in each estimate less than Final Estimates of Parameters Type Coef SE Coef T P AR AR Constant Mean Number of observations: 13 Residuals: SS = (backforecasts excluded) MS = DF = 10 Modified Box-Pierce (Ljung-Box) Chi-Square statistic Lag Chi-Square 12.9 * * * DF 9 * * * P-Value * * * Forecasts from period 13 95% Limits Period Forecast Lower Upper Actual

19 5. ARIMA (0,0,2) L19

20 L20

21 L21 ARIMA Model: Jakarta-Muscat Estimates at each iteration Iteration SSE Parameters

22 L Relative change in each estimate less than Final Estimates of Parameters Type Coef SE Coef T P MA MA Constant Mean Number of observations: 13 Residuals: SS = (backforecasts excluded) MS = DF = 10 Modified Box-Pierce (Ljung-Box) Chi-Square statistic Lag Chi-Square 9.3 * * * DF 9 * * * P-Value * * * Forecasts from period 13 95% Limits Period Forecast Lower Upper Actual

23 6. ARIMA (1,0,2) L23

24 L24 ARIMA Model: Jakarta-Muscat Estimates at each iteration Iteration SSE Parameters ** Convergence criterion not met after 25 iterations ** Final Estimates of Parameters

25 L25 Type Coef SE Coef T P AR MA MA Constant Mean Number of observations: 13 Residuals: SS = (backforecasts excluded) MS = DF = 9 Modified Box-Pierce (Ljung-Box) Chi-Square statistic Lag Chi-Square 7.8 * * * DF 8 * * * P-Value * * * Forecasts from period 13 95% Limits Period Forecast Lower Upper Actual ARIMA (2,0,1)

26 L26 ARIMA Model: Jakarta-Muscat Estimates at each iteration Iteration SSE Parameters

27 L Relative change in each estimate less than Final Estimates of Parameters Type Coef SE Coef T P AR AR MA Constant Mean Number of observations: 13 Residuals: SS = (backforecasts excluded) MS = DF = 9 Modified Box-Pierce (Ljung-Box) Chi-Square statistic Lag Chi-Square 12.5 * * * DF 8 * * * P-Value * * * Forecasts from period 13 95% Limits Period Forecast Lower Upper Actual

28 8. ARIMA (2,0,2) L28

29 L29 ARIMA Model: Jakarta-Muscat Estimates at each iteration Iteration SSE Parameters Unable to reduce sum of squares any further Final Estimates of Parameters

30 L30 Type Coef SE Coef T P AR AR MA MA Constant Mean Number of observations: 13 Residuals: SS = (backforecasts excluded) MS = DF = 8 Modified Box-Pierce (Ljung-Box) Chi-Square statistic Lag Chi-Square 7.5 * * * DF 7 * * * P-Value * * * Forecasts from period 13 95% Limits Period Forecast Lower Upper Actual ARIMA (3,0,0)

31 L31 ARIMA Model: Jakarta-Muscat Estimates at each iteration Iteration SSE Parameters

32 L Relative change in each estimate less than Final Estimates of Parameters Type Coef SE Coef T P AR AR AR Constant Mean Number of observations: 13 Residuals: SS = (backforecasts excluded) MS = DF = 9 Modified Box-Pierce (Ljung-Box) Chi-Square statistic Lag Chi-Square 11.5 * * * DF 8 * * * P-Value * * * Forecasts from period 13 95% Limits Period Forecast Lower Upper Actual

33 10. ARIMA (0,0,3) L33

34 L34 ARIMA Model: Jakarta-Muscat Estimates at each iteration Iteration SSE Parameters Relative change in each estimate less than Final Estimates of Parameters Type Coef SE Coef T P MA MA MA Constant Mean

35 L35 Number of observations: 13 Residuals: SS = (backforecasts excluded) MS = DF = 9 Modified Box-Pierce (Ljung-Box) Chi-Square statistic Lag Chi-Square 8.3 * * * DF 8 * * * P-Value * * * Forecasts from period 13 95% Limits Period Forecast Lower Upper Actual ARIMA (1,0,3)

36 L36 ARIMA Model: Jakarta-Muscat Estimates at each iteration Iteration SSE Parameters

37 L ** Convergence criterion not met after 25 iterations ** Final Estimates of Parameters Type Coef SE Coef T P AR MA MA MA Constant Mean Number of observations: 13 Residuals: SS = (backforecasts excluded) MS = DF = 8 Modified Box-Pierce (Ljung-Box) Chi-Square statistic Lag Chi-Square 10.9 * * * DF 7 * * * P-Value * * * Forecasts from period 13 95% Limits Period Forecast Lower Upper Actual

38 12. ARIMA (3,0,1) L38

39 L39 ARIMA Model: Jakarta-Muscat Estimates at each iteration Iteration SSE Parameters ** Convergence criterion not met after 25 iterations ** Final Estimates of Parameters

40 L40 Type Coef SE Coef T P AR AR AR MA Constant Mean Number of observations: 13 Residuals: SS = (backforecasts excluded) MS = DF = 8 Modified Box-Pierce (Ljung-Box) Chi-Square statistic Lag Chi-Square 11.0 * * * DF 7 * * * P-Value * * * Forecasts from period 13 95% Limits Period Forecast Lower Upper Actual ARIMA (2,0,3)

41 L41 ARIMA Model: Jakarta-Muscat Estimates at each iteration Iteration SSE Parameters

42 L Relative change in each estimate less than Final Estimates of Parameters Type Coef SE Coef T P AR AR MA MA MA Constant Mean Number of observations: 13 Residuals: SS = (backforecasts excluded) MS = DF = 7 Modified Box-Pierce (Ljung-Box) Chi-Square statistic Lag Chi-Square 12.0 * * * DF 6 * * * P-Value * * * Forecasts from period 13 95% Limits Period Forecast Lower Upper Actual Period Forecast Lower Upper Actual

43 L ARIMA (3,0,2) ARIMA Model: Jakarta-Muscat Estimates at each iteration Iteration SSE Parameters Relative change in each estimate less than * ERROR * Model cannot be estimated with these data.

44 15. ARIMA (3,0,3) L44

45 L45 ARIMA Model: Jakarta-Muscat Estimates at each iteration Iteration SSE Parameters

46 L ** Convergence criterion not met after 25 iterations ** Final Estimates of Parameters Type Coef SE Coef T P AR AR AR MA MA MA Constant Mean Number of observations: 13 Residuals: SS = (backforecasts excluded) MS = DF = 6 Modified Box-Pierce (Ljung-Box) Chi-Square statistic Lag Chi-Square 7.6 * * * DF 5 * * * P-Value * * * Forecasts from period 13 95% Limits Period Forecast Lower Upper Actual

47 16. ARIMA (4,0,0) L47

48 L48 ARIMA Model: Jakarta-Muscat Estimates at each iteration Iteration SSE Parameters

49 L49 ** Convergence criterion not met after 25 iterations ** * WARNING * Back forecasts not dying out rapidly Back forecasts (after differencing) Lag Lag Lag Lag Lag Lag Lag Lag Lag Lag Lag Lag Lag Lag Lag Lag Back forecast residuals Lag Lag Lag Lag Lag Lag Lag Lag Lag Lag Lag Lag Lag Lag Lag Lag Final Estimates of Parameters Type Coef SE Coef T P AR AR AR AR Constant Mean Number of observations: 13 Residuals: SS = (backforecasts excluded) MS = DF = 8 Modified Box-Pierce (Ljung-Box) Chi-Square statistic

50 L50 Lag Chi-Square 4.9 * * * DF 7 * * * P-Value * * * Forecasts from period 13 95% Limits Period Forecast Lower Upper Actual ARIMA (0,0,4)

51 L51 ARIMA Model: Jakarta-Muscat Estimates at each iteration Iteration SSE Parameters

52 L ** Convergence criterion not met after 25 iterations ** Final Estimates of Parameters Type Coef SE Coef T P MA MA MA MA Constant Mean Number of observations: 13 Residuals: SS = (backforecasts excluded) MS = DF = 8 Modified Box-Pierce (Ljung-Box) Chi-Square statistic Lag Chi-Square 12.4 * * * DF 7 * * * P-Value * * * Forecasts from period 13 95% Limits Period Forecast Lower Upper Actual

53 18. ARIMA (1,0,4) L53

54 L54 ARIMA Model: Jakarta-Muscat Estimates at each iteration Iteration SSE Parameters

55 L55 ** Convergence criterion not met after 25 iterations ** Final Estimates of Parameters Type Coef SE Coef T P AR MA MA MA MA Constant Mean Number of observations: 13 Residuals: SS = (backforecasts excluded) MS = DF = 7 Modified Box-Pierce (Ljung-Box) Chi-Square statistic Lag Chi-Square 12.4 * * * DF 6 * * * P-Value * * * Forecasts from period 13 95% Limits Period Forecast Lower Upper Actual ARIMA (2,0,4)

56 L56 ARIMA Model: Jakarta-Muscat Estimates at each iteration Iteration SSE Parameters

57 L ** Convergence criterion not met after 25 iterations ** * WARNING * Back forecasts not dying out rapidly Back forecasts (after differencing) Lag Lag Lag Lag Lag

58 L58 Lag Lag Lag Lag Lag Lag Lag Lag Lag Lag Lag Back forecast residuals Lag Lag Lag Lag Lag Lag Lag Lag Lag Lag Lag Lag Lag Lag Lag Lag Final Estimates of Parameters Type Coef SE Coef T P AR AR MA MA MA MA Constant Mean Number of observations: 13 Residuals: SS = (backforecasts excluded) MS = DF = 6 Modified Box-Pierce (Ljung-Box) Chi-Square statistic Lag Chi-Square 13.9 * * * DF 5 * * * P-Value * * * Forecasts from period 13 95% Limits Period Forecast Lower Upper Actual

59 L ARIMA (3,0,4) ARIMA Model: Jakarta-Muscat Estimates at each iteration Iteration SSE Parameters

60 L ** Convergence criterion not met after 25 iterations ** * ERROR * Model cannot be estimated with these data. 21. ARIMA (4,0,4)

61 L61 ARIMA Model: Jakarta-Muscat Estimates at each iteration Iteration SSE Parameters ** Convergence criterion not met after 25 iterations **

62 L62 * ERROR * Model cannot be estimated with these data. 22. ARIMA (4,0,1)

63 L63 ARIMA Model: Jakarta-Muscat Estimates at each iteration Iteration SSE Parameters ** Convergence criterion not met after 25 iterations **

64 L64 Final Estimates of Parameters Type Coef SE Coef T P AR AR AR AR MA Constant Mean Number of observations: 13 Residuals: SS = (backforecasts excluded) MS = DF = 7 Modified Box-Pierce (Ljung-Box) Chi-Square statistic Lag Chi-Square 11.6 * * * DF 6 * * * P-Value * * * Forecasts from period 13 95% Limits Period Forecast Lower Upper Actual ARIMA (4,0,2)

65 L65 ARIMA Model: Jakarta-Muscat Estimates at each iteration Iteration SSE Parameters Relative change in each estimate less than * ERROR * Model cannot be estimated with these data.

66 L ARIMA (4,0,3) ARIMA Model: Jakarta-Muscat Estimates at each iteration Iteration SSE Parameters

67 L Relative change in each estimate less than * ERROR * Model cannot be estimated with these data. 25. ARIMA (5,0,0)

68 L68

69 L69 ARIMA Model: Jakarta-Muscat Estimates at each iteration Iteration SSE Parameters ** Convergence criterion not met after 25 iterations ** * WARNING * Back forecasts not dying out rapidly Back forecasts (after differencing) Lag Lag Lag Lag Lag Lag Lag Lag Lag Lag Lag Lag Lag Lag Lag Lag Back forecast residuals Lag Lag Lag

70 L70 Lag Lag Lag Lag Lag Lag Lag Lag Lag Lag Lag Lag Lag Final Estimates of Parameters Type Coef SE Coef T P AR AR AR AR AR Constant Mean Number of observations: 13 Residuals: SS = (backforecasts excluded) MS = DF = 7 Modified Box-Pierce (Ljung-Box) Chi-Square statistic Lag Chi-Square 10.3 * * * DF 6 * * * P-Value * * * Forecasts from period 13 95% Limits Period Forecast Lower Upper Actual

71 26. ARIMA (0,0,5) L71

72 L72 ARIMA Model: Jakarta-Muscat Estimates at each iteration Iteration SSE Parameters ** Convergence criterion not met after 25 iterations **

73 L73 Final Estimates of Parameters Type Coef SE Coef T P MA MA MA MA MA Constant Mean Number of observations: 13 Residuals: SS = (backforecasts excluded) MS = DF = 7 Modified Box-Pierce (Ljung-Box) Chi-Square statistic Lag Chi-Square 8.2 * * * DF 6 * * * P-Value * * * Forecasts from period 13 95% Limits Period Forecast Lower Upper Actual ARIMA (1,0,5)

74 L74

75 L75 ARIMA Model: Jakarta-Muscat Estimates at each iteration Iteration SSE Parameters ** Convergence criterion not met after 25 iterations **

76 L76 Final Estimates of Parameters Type Coef SE Coef T P AR MA MA MA MA MA Constant Mean Number of observations: 13 Residuals: SS = (backforecasts excluded) MS = DF = 6 Modified Box-Pierce (Ljung-Box) Chi-Square statistic Lag Chi-Square 7.1 * * * DF 5 * * * P-Value * * * Forecasts from period 13 95% Limits Period Forecast Lower Upper Actual ARIMA (2,0,5)

77 L77

78 L78 ARIMA Model: Jakarta-Muscat Estimates at each iteration Iteration SSE Parameters