Use the moving average method to forecast period 105.
Use the exponential smoothing method to forecast period 105.
Use the time-series decomposition method to forecast period 105.
Comparing the three methods, which one fits this situation best?
The larger the parameter (n) is set, the more historical data are taken into account by the moving average.
You can choose different parameter (n) to extrapolate to compare the prediction effect.
In general, the parameter (n) should not be taken too large.
Moving average.
1 | 530 |
2 | 570 |
3 | 560 |
4 | 530 |
5 | 510 |
6 | 560 |
7 | 610 |
8 | 560 |
9 | 580 |
10 | 610 |
11 | 650 |
12 | 700 |
13 | 670 |
14 | 700 |
15 | 760 |
16 | 730 |
17 | 760 |
18 | 820 |
19 | 780 |
20 | 900 |
21 | 840 |
22 | 770 |
23 | 820 |
24 | 800 |
25 | 760 |
26 | 760 |
27 | 770 |
28 | 790 |
29 | 760 |
30 | 740 |
31 | 720 |
32 | 670 |
33 | 690 |
34 | 470 |
35 | 670 |
36 | 690 |
37 | 620 |
38 | 650 |
39 | 610 |
40 | 620 |
41 | 640 |
42 | 590 |
43 | 610 |
44 | 600 |
45 | 630 |
46 | 600 |
47 | 630 |
48 | 640 |
49 | 610 |
50 | 590 |
51 | 610 |
52 | 630 |
53 | 660 |
54 | 640 |
55 | 810 |
56 | 790 |
57 | 820 |
58 | 650 |
59 | 710 |
60 | 700 |
61 | 670 |
62 | 690 |
63 | 730 |
64 | 730 |
65 | 760 |
66 | 790 |
67 | 810 |
68 | 870 |
69 | 890 |
70 | 870 |
71 | 890 |
72 | 880 |
73 | 930 |
74 | 980 |
75 | 900 |
76 | 860 |
77 | 890 |
78 | 880 |
79 | 870 |
80 | 840 |
81 | 860 |
82 | 910 |
83 | 870 |
84 | 860 |
85 | 840 |
86 | 540 |
87 | 780 |
88 | 750 |
89 | 780 |
90 | 760 |
91 | 710 |
92 | 730 |
93 | 750 |
94 | 750 |
95 | 710 |
96 | 750 |
97 | 720 |
98 | 770 |
99 | 740 |
100 | 750 |
101 | 760 |
102 | 780 |
103 | 800 |
104 | 850 |
105 |
Exponential smoothing
same
1 | 530 |
2 | 570 |
3 | 560 |
4 | 530 |
5 | 510 |
6 | 560 |
7 | 610 |
8 | 560 |
9 | 580 |
10 | 610 |
11 | 650 |
12 | 700 |
13 | 670 |
14 | 700 |
15 | 760 |
16 | 730 |
17 | 760 |
18 | 820 |
19 | 780 |
20 | 900 |
21 | 840 |
22 | 770 |
23 | 820 |
24 | 800 |
25 | 760 |
26 | 760 |
27 | 770 |
28 | 790 |
29 | 760 |
30 | 740 |
31 | 720 |
32 | 670 |
33 | 690 |
34 | 470 |
35 | 670 |
36 | 690 |
37 | 620 |
38 | 650 |
39 | 610 |
40 | 620 |
41 | 640 |
42 | 590 |
43 | 610 |
44 | 600 |
45 | 630 |
46 | 600 |
47 | 630 |
48 | 640 |
49 | 610 |
50 | 590 |
51 | 610 |
52 | 630 |
53 | 660 |
54 | 640 |
55 | 810 |
56 | 790 |
57 | 820 |
58 | 650 |
59 | 710 |
60 | 700 |
61 | 670 |
62 | 690 |
63 | 730 |
64 | 730 |
65 | 760 |
66 | 790 |
67 | 810 |
68 | 870 |
69 | 890 |
70 | 870 |
71 | 890 |
72 | 880 |
73 | 930 |
74 | 980 |
75 | 900 |
76 | 860 |
77 | 890 |
78 | 880 |
79 | 870 |
80 | 840 |
81 | 860 |
82 | 910 |
83 | 870 |
84 | 860 |
85 | 840 |
86 | 540 |
87 | 780 |
88 | 750 |
89 | 780 |
90 | 760 |
91 | 710 |
92 | 730 |
93 | 750 |
94 | 750 |
95 | 710 |
96 | 750 |
97 | 720 |
98 | 770 |
99 | 740 |
100 | 750 |
101 | 760 |
102 | 780 |
103 | 800 |
104 | 850 |
105 |
Time-series decomposition
1 | 530 |
2 | 570 |
3 | 560 |
4 | 530 |
5 | 510 |
6 | 560 |
7 | 610 |
8 | 560 |
9 | 580 |
10 | 610 |
11 | 650 |
12 | 700 |
13 | 670 |
14 | 700 |
15 | 760 |
16 | 730 |
17 | 760 |
18 | 820 |
19 | 780 |
20 | 900 |
21 | 840 |
22 | 770 |
23 | 820 |
24 | 800 |
25 | 760 |
26 | 760 |
27 | 770 |
28 | 790 |
29 | 760 |
30 | 740 |
31 | 720 |
32 | 670 |
33 | 690 |
34 | 470 |
35 | 670 |
36 | 690 |
37 | 620 |
38 | 650 |
39 | 610 |
40 | 620 |
41 | 640 |
42 | 590 |
43 | 610 |
44 | 600 |
45 | 630 |
46 | 600 |
47 | 630 |
48 | 640 |
49 | 610 |
50 | 590 |
51 | 610 |
52 | 630 |
53 | 660 |
54 | 640 |
55 | 810 |
56 | 790 |
57 | 820 |
58 | 650 |
59 | 710 |
60 | 700 |
61 | 670 |
62 | 690 |
63 | 730 |
64 | 730 |
65 | 760 |
66 | 790 |
67 | 810 |
68 | 870 |
69 | 890 |
70 | 870 |
71 | 890 |
72 | 880 |
73 | 930 |
74 | 980 |
75 | 900 |
76 | 860 |
77 | 890 |
78 | 880 |
79 | 870 |
80 | 840 |
81 | 860 |
82 | 910 |
83 | 870 |
84 | 860 |
85 | 840 |
86 | 540 |
87 | 780 |
88 | 750 |
89 | 780 |
90 | 760 |
91 | 710 |
92 | 730 |
93 | 750 |
94 | 750 |
95 | 710 |
96 | 750 |
97 | 720 |
98 | 770 |
99 | 740 |
100 | 750 |
101 | 760 |
102 | 780 |
103 | 800 |
104 | 850 |
105 |
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