Forecasting method

Question

Question

Forecasting method

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

Industrial-Engineering

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Answer 1

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Forecasting method

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The following is a random sample of n = 90 undergraduate students' annual textbook expense. 610...

I just need help with questions 5 and 6 370 300 630 530 Next SIX questions...

Next SIX questions are related to the following data: The following is a random sample of...

To test the effectiveness of a business school preparation course, 8 students took a general business...

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Below are monthly rents paid by 30 students who live off campus. 730 730 730 930...

Monthly rent paid by undergraduates and graduate students. Undergraduate Student Rents (n = 10) 800 750...

Problem 1 The yearly streamflow record of a stream and the corresponding average annual precipi- tation...

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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

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

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

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

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

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

Forecasting method

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Forecasting method

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

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

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