Last data update: 2014.03.03
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R: Comparing and sort Abbasov-Mamedova models
Abbasov.Cs2 | R Documentation |
Comparing and sort Abbasov-Mamedova models
Description
Comparing and sort Abbasov-Mamedova models according ME, MAE, MPE, MAPE, MSE RMSE for C values in Cs.
Usage
Abbasov.Cs2(ts, n = 7, w = 7, D1 = 0, D2 = 0, Cs = NULL)
Arguments
ts |
Observation series.
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n |
Number of fuzzy set.
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w |
'w' parameter.
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D1 |
A adequate value.
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D2 |
A adequate value.
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Cs |
A vector contain C values.
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Details
Now, this documen are updating.
Value
Table comparing and sort Abbasov-Mamedova models.
Author(s)
Hong Viet Minh <hongvietminh@gmail.com>
Vo Van Tai <vvtai@ctu.edu.vn>
See Also
FindC2 ,FindC3 ,Abbasov.Cs3
Examples
Abbasov.Cs2(lh,n=5,w=7,Cs=seq(0,1,0.01))
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(AnalyzeTS)
Loading required package: MASS
Loading required package: TSA
Loading required package: leaps
Loading required package: locfit
locfit 1.5-9.1 2013-03-22
Loading required package: mgcv
Loading required package: nlme
This is mgcv 1.8-12. For overview type 'help("mgcv-package")'.
Loading required package: tseries
Attaching package: 'TSA'
The following objects are masked from 'package:stats':
acf, arima
The following object is masked from 'package:utils':
tar
Loading required package: TTR
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/AnalyzeTS/Abbasov.Cs2.Rd_%03d_medium.png", width=480, height=480)
> ### Name: Abbasov.Cs2
> ### Title: Comparing and sort Abbasov-Mamedova models
> ### Aliases: Abbasov.Cs2
> ### Keywords: Abbasov.Cs2
>
> ### ** Examples
>
> Abbasov.Cs2(lh,n=5,w=7,Cs=seq(0,1,0.01))
C values ME MAE MPE MAPE MSE RMSE MExl
1 0.00 -0.3350000 0.5575000 -16.79651 24.26311 0.3830000 0.6188699 1
2 0.01 -0.3349754 0.5574867 -16.79529 24.26231 0.3829850 0.6188578 2
3 0.02 -0.3349015 0.5574467 -16.79166 24.25992 0.3829399 0.6188214 3
4 0.03 -0.3347785 0.5573802 -16.78560 24.25593 0.3828649 0.6187608 4
5 0.04 -0.3346065 0.5572871 -16.77713 24.25037 0.3827602 0.6186762 5
6 0.05 -0.3343858 0.5571677 -16.76627 24.24322 0.3826261 0.6185678 6
7 0.06 -0.3341168 0.5570222 -16.75302 24.23451 0.3824630 0.6184360 7
8 0.07 -0.3337997 0.5568507 -16.73742 24.22425 0.3822714 0.6182810 8
9 0.08 -0.3334352 0.5566534 -16.71948 24.21246 0.3820518 0.6181034 9
10 0.09 -0.3330237 0.5564307 -16.69923 24.19914 0.3818049 0.6179036 10
11 0.10 -0.3325657 0.5561829 -16.67670 24.18433 0.3815312 0.6176821 11
12 0.11 -0.3320621 0.5559104 -16.65192 24.16804 0.3812317 0.6174396 12
13 0.12 -0.3315133 0.5556134 -16.62493 24.15030 0.3809070 0.6171766 13
14 0.13 -0.3309203 0.5552924 -16.59576 24.13114 0.3805581 0.6168939 14
15 0.14 -0.3302838 0.5549478 -16.56447 24.11057 0.3801859 0.6165922 15
16 0.15 -0.3296047 0.5545802 -16.53109 24.08864 0.3797914 0.6162722 16
17 0.16 -0.3288839 0.5541899 -16.49566 24.06537 0.3793755 0.6159347 17
18 0.17 -0.3281223 0.5537774 -16.45824 24.04079 0.3789394 0.6155805 18
19 0.18 -0.3273209 0.5533434 -16.41887 24.01494 0.3784840 0.6152106 19
20 0.19 -0.3264807 0.5528883 -16.37761 23.98784 0.3780105 0.6148256 20
21 0.20 -0.3256028 0.5524127 -16.33451 23.95954 0.3775201 0.6144266 21
22 0.21 -0.3246882 0.5519172 -16.28961 23.93008 0.3770137 0.6140144 22
23 0.22 -0.3237381 0.5514023 -16.24299 23.89947 0.3764927 0.6135900 23
24 0.23 -0.3227534 0.5508686 -16.19468 23.86778 0.3759580 0.6131541 24
25 0.24 -0.3217355 0.5503168 -16.14476 23.83502 0.3754109 0.6127078 25
26 0.25 -0.3206853 0.5497475 -16.09327 23.80125 0.3748525 0.6122520 26
27 0.26 -0.3196040 0.5491612 -16.04027 23.76649 0.3742839 0.6117874 27
28 0.27 -0.3184928 0.5485585 -15.98583 23.73079 0.3737062 0.6113151 28
29 0.28 -0.3173528 0.5479402 -15.92999 23.69418 0.3731205 0.6108359 29
30 0.29 -0.3161853 0.5473068 -15.87281 23.65670 0.3725279 0.6103507 30
31 0.30 -0.3149912 0.5466589 -15.81436 23.61839 0.3719295 0.6098602 31
32 0.31 -0.3137718 0.5459971 -15.75469 23.57929 0.3713263 0.6093655 32
33 0.32 -0.3125282 0.5453221 -15.69385 23.53943 0.3707193 0.6088672 33
34 0.33 -0.3112616 0.5446345 -15.63190 23.49885 0.3701094 0.6083662 34
35 0.34 -0.3099730 0.5440517 -15.56890 23.46209 0.3694976 0.6078632 35
36 0.35 -0.3086635 0.5434751 -15.50491 23.42536 0.3688849 0.6073590 36
37 0.36 -0.3073343 0.5428894 -15.43996 23.38809 0.3682721 0.6068542 37
38 0.37 -0.3059863 0.5422952 -15.37413 23.35031 0.3676600 0.6063497 38
39 0.38 -0.3046207 0.5416927 -15.30745 23.31204 0.3670495 0.6058461 39
40 0.39 -0.3032385 0.5410826 -15.23998 23.27332 0.3664413 0.6053439 40
41 0.40 -0.3018406 0.5404652 -15.17178 23.23418 0.3658361 0.6048439 41
42 0.41 -0.3004281 0.5398410 -15.10288 23.19464 0.3652348 0.6043466 42
43 0.42 -0.2990019 0.5392103 -15.03334 23.15473 0.3646379 0.6038525 43
44 0.43 -0.2975630 0.5385736 -14.96319 23.11448 0.3640461 0.6033623 44
45 0.44 -0.2961123 0.5379314 -14.89250 23.07391 0.3634599 0.6028764 45
46 0.45 -0.2946506 0.5372838 -14.82129 23.03305 0.3628800 0.6023952 46
47 0.46 -0.2931789 0.5366315 -14.74962 22.99192 0.3623068 0.6019193 47
48 0.47 -0.2916979 0.5359747 -14.67752 22.95054 0.3617409 0.6014490 48
49 0.48 -0.2902085 0.5353138 -14.60504 22.90895 0.3611828 0.6009848 49
50 0.49 -0.2887116 0.5346491 -14.53221 22.86715 0.3606327 0.6005270 50
51 0.50 -0.2872078 0.5339810 -14.45907 22.82517 0.3600912 0.6000760 51
52 0.51 -0.2856979 0.5333099 -14.38565 22.78304 0.3595587 0.5996321 52
53 0.52 -0.2841826 0.5326359 -14.31199 22.74077 0.3590354 0.5991956 53
54 0.53 -0.2826626 0.5319596 -14.23813 22.69838 0.3585216 0.5987668 54
55 0.54 -0.2811386 0.5312810 -14.16410 22.65589 0.3580177 0.5983458 55
56 0.55 -0.2796113 0.5306006 -14.08992 22.61332 0.3575239 0.5979331 56
57 0.56 -0.2780812 0.5299187 -14.01563 22.57069 0.3570405 0.5975286 57
58 0.57 -0.2765489 0.5292354 -13.94126 22.52800 0.3565676 0.5971328 58
59 0.58 -0.2750151 0.5285511 -13.86684 22.48528 0.3561054 0.5967456 59
60 0.59 -0.2734802 0.5278659 -13.79238 22.44255 0.3556541 0.5963674 60
61 0.60 -0.2719448 0.5271802 -13.71792 22.39981 0.3552138 0.5959981 61
62 0.61 -0.2704094 0.5264942 -13.64348 22.35709 0.3547846 0.5956380 62
63 0.62 -0.2688746 0.5258081 -13.56909 22.31438 0.3543667 0.5952871 63
64 0.63 -0.2673407 0.5251221 -13.49476 22.27172 0.3539601 0.5949455 64
65 0.64 -0.2658082 0.5244364 -13.42053 22.22910 0.3535649 0.5946132 65
66 0.65 -0.2642776 0.5237512 -13.34640 22.18655 0.3531811 0.5942905 66
67 0.66 -0.2627491 0.5230668 -13.27240 22.14407 0.3528088 0.5939771 67
68 0.67 -0.2612234 0.5223832 -13.19854 22.10167 0.3524480 0.5936733 68
69 0.68 -0.2597006 0.5217006 -13.12485 22.05936 0.3520987 0.5933790 69
70 0.69 -0.2581812 0.5210193 -13.05134 22.01716 0.3517608 0.5930942 70
71 0.70 -0.2566655 0.5203394 -12.97803 21.97507 0.3514344 0.5928190 71
72 0.71 -0.2551538 0.5196610 -12.90493 21.93310 0.3511194 0.5925532 72
73 0.72 -0.2536464 0.5189842 -12.83206 21.89126 0.3508157 0.5922970 73
74 0.73 -0.2521437 0.5183093 -12.75943 21.84955 0.3505234 0.5920502 74
75 0.74 -0.2506458 0.5176363 -12.68705 21.80799 0.3502423 0.5918127 75
76 0.75 -0.2491530 0.5169654 -12.61493 21.76658 0.3499724 0.5915847 76
77 0.76 -0.2476657 0.5162966 -12.54310 21.72533 0.3497136 0.5913659 77
78 0.77 -0.2461839 0.5156301 -12.47155 21.68424 0.3494658 0.5911563 78
79 0.78 -0.2447080 0.5149660 -12.40030 21.64332 0.3492289 0.5909559 79
80 0.79 -0.2432382 0.5143044 -12.32935 21.60257 0.3490029 0.5907646 80
81 0.80 -0.2417746 0.5136454 -12.25873 21.56201 0.3487875 0.5905823 81
82 0.81 -0.2403174 0.5129891 -12.18843 21.52163 0.3485827 0.5904089 82
83 0.82 -0.2388668 0.5123355 -12.11846 21.48144 0.3483884 0.5902443 83
84 0.83 -0.2374229 0.5116847 -12.04883 21.44144 0.3482044 0.5900885 84
85 0.84 -0.2359860 0.5110368 -11.97955 21.40164 0.3480306 0.5899412 85
86 0.85 -0.2345561 0.5103920 -11.91063 21.36204 0.3478670 0.5898025 86
87 0.86 -0.2331334 0.5097501 -11.84206 21.32265 0.3477132 0.5896721 87
88 0.87 -0.2317180 0.5091114 -11.77386 21.28347 0.3475693 0.5895501 88
89 0.88 -0.2303101 0.5084758 -11.70604 21.24450 0.3474351 0.5894363 89
90 0.89 -0.2289096 0.5078434 -11.63859 21.20574 0.3473104 0.5893305 90
91 0.90 -0.2275168 0.5072143 -11.57151 21.16720 0.3471952 0.5892327 91
92 0.91 -0.2261316 0.5065885 -11.50483 21.12887 0.3470891 0.5891427 92
93 0.92 -0.2247543 0.5059661 -11.43853 21.09077 0.3469922 0.5890604 93
94 0.93 -0.2233848 0.5053470 -11.37262 21.05289 0.3469042 0.5889858 94
95 0.94 -0.2220233 0.5047313 -11.30711 21.01524 0.3468250 0.5889185 95
96 0.95 -0.2206697 0.5041191 -11.24199 20.97781 0.3467545 0.5888587 96
97 0.96 -0.2193242 0.5035104 -11.17727 20.94061 0.3466925 0.5888060 97
98 0.97 -0.2179869 0.5029052 -11.11296 20.90363 0.3466389 0.5887605 98
99 0.98 -0.2166576 0.5023035 -11.04905 20.86689 0.3465935 0.5887219 99
100 0.99 -0.2153366 0.5017054 -10.98554 20.83038 0.3465561 0.5886902 100
101 1.00 -0.2140237 0.5011108 -10.92244 20.79409 0.3465267 0.5886652 101
MAExl MPExl MAPExl MSExl RMSExl
1 101 1 101 101 101
2 100 2 100 100 100
3 99 3 99 99 99
4 98 4 98 98 98
5 97 5 97 97 97
6 96 6 96 96 96
7 95 7 95 95 95
8 94 8 94 94 94
9 93 9 93 93 93
10 92 10 92 92 92
11 91 11 91 91 91
12 90 12 90 90 90
13 89 13 89 89 89
14 88 14 88 88 88
15 87 15 87 87 87
16 86 16 86 86 86
17 85 17 85 85 85
18 84 18 84 84 84
19 83 19 83 83 83
20 82 20 82 82 82
21 81 21 81 81 81
22 80 22 80 80 80
23 79 23 79 79 79
24 78 24 78 78 78
25 77 25 77 77 77
26 76 26 76 76 76
27 75 27 75 75 75
28 74 28 74 74 74
29 73 29 73 73 73
30 72 30 72 72 72
31 71 31 71 71 71
32 70 32 70 70 70
33 69 33 69 69 69
34 68 34 68 68 68
35 67 35 67 67 67
36 66 36 66 66 66
37 65 37 65 65 65
38 64 38 64 64 64
39 63 39 63 63 63
40 62 40 62 62 62
41 61 41 61 61 61
42 60 42 60 60 60
43 59 43 59 59 59
44 58 44 58 58 58
45 57 45 57 57 57
46 56 46 56 56 56
47 55 47 55 55 55
48 54 48 54 54 54
49 53 49 53 53 53
50 52 50 52 52 52
51 51 51 51 51 51
52 50 52 50 50 50
53 49 53 49 49 49
54 48 54 48 48 48
55 47 55 47 47 47
56 46 56 46 46 46
57 45 57 45 45 45
58 44 58 44 44 44
59 43 59 43 43 43
60 42 60 42 42 42
61 41 61 41 41 41
62 40 62 40 40 40
63 39 63 39 39 39
64 38 64 38 38 38
65 37 65 37 37 37
66 36 66 36 36 36
67 35 67 35 35 35
68 34 68 34 34 34
69 33 69 33 33 33
70 32 70 32 32 32
71 31 71 31 31 31
72 30 72 30 30 30
73 29 73 29 29 29
74 28 74 28 28 28
75 27 75 27 27 27
76 26 76 26 26 26
77 25 77 25 25 25
78 24 78 24 24 24
79 23 79 23 23 23
80 22 80 22 22 22
81 21 81 21 21 21
82 20 82 20 20 20
83 19 83 19 19 19
84 18 84 18 18 18
85 17 85 17 17 17
86 16 86 16 16 16
87 15 87 15 15 15
88 14 88 14 14 14
89 13 89 13 13 13
90 12 90 12 12 12
91 11 91 11 11 11
92 10 92 10 10 10
93 9 93 9 9 9
94 8 94 8 8 8
95 7 95 7 7 7
96 6 96 6 6 6
97 5 97 5 5 5
98 4 98 4 4 4
99 3 99 3 3 3
100 2 100 2 2 2
101 1 101 1 1 1
>
>
>
>
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> dev.off()
null device
1
>
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