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Decompositie omzet product x

*Unverified author*

R Software Module: /rwasp_decompose.wasp (opens new window with default values)

Title produced by software: Classical Decomposition

Date of computation: Fri, 30 Jul 2010 13:21:57 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Jul/30/t1280496181blpkby4hf9fb6c8.htm/, Retrieved Fri, 30 Jul 2010 15:23:07 +0200

BibTeX entries for LaTeX users:

@Manual{KEY,
    author = {{YOUR NAME}},
    publisher = {Office for Research Development and Education},
    title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Jul/30/t1280496181blpkby4hf9fb6c8.htm/},
    year = {2010},
}
@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Development Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2010},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

56 55 54 52 72 71 56 46 47 47 48 50 44 38 33 33 52 54 39 22 31 31 38 42 41 31 36 34 51 47 31 19 30 33 36 40 32 25 28 29 55 55 40 38 44 41 49 59 61 47 43 39 66 68 63 68 67 59 68 78 82 70 62 68 94 102 100 104 103 93 110 114 120 102 95 103 122 139 135 135 137 130 148 148 145 128 131 133 146 163 151 157 152 149 172 167 160 150 160 165 171 179 171 176 170 169 194 196 188 174 186 191 197 206 197 204 201 190 213 213

Output produced by software:

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	56	NA	NA	3.88271604938272	NA
2	55	NA	NA	-9.50154320987654	NA
3	54	NA	NA	-9.945987654321	NA
4	52	NA	NA	-8.98765432098766	NA
5	72	NA	NA	7.25308641975309	NA
6	71	NA	NA	12.2901234567901	NA
7	56	55.4984567901235	54	1.49845679012345	0.501543209876544
8	46	50.7947530864197	52.7916666666667	-1.99691358024692	-4.79475308641975
9	47	49.8271604938272	51.2083333333333	-1.38117283950618	-2.82716049382716
10	47	43.6836419753086	49.5416666666667	-5.85802469135802	3.31635802469135
11	48	53.1697530864198	47.9166666666667	5.25308641975309	-5.16975308641975
12	50	53.8688271604938	46.375	7.49382716049383	-3.86882716049383
13	44	48.8410493827161	44.9583333333333	3.88271604938272	-4.84104938271606
14	38	33.7484567901235	43.25	-9.50154320987654	4.25154320987654
15	33	31.6373456790123	41.5833333333333	-9.945987654321	1.36265432098766
16	33	31.2623456790123	40.25	-8.98765432098766	1.73765432098766
17	52	46.4197530864197	39.1666666666667	7.25308641975309	5.58024691358025
18	54	50.7067901234568	38.4166666666667	12.2901234567901	3.29320987654321
19	39	39.4567901234568	37.9583333333333	1.49845679012345	-0.456790123456784
20	22	35.5447530864197	37.5416666666667	-1.99691358024692	-13.5447530864197
21	31	35.9938271604938	37.375	-1.38117283950618	-4.99382716049382
22	31	31.6836419753086	37.5416666666667	-5.85802469135802	-0.683641975308646
23	38	42.7947530864198	37.5416666666667	5.25308641975309	-4.79475308641975
24	42	44.7021604938272	37.2083333333333	7.49382716049383	-2.70216049382716
25	41	40.4660493827161	36.5833333333333	3.88271604938272	0.533950617283942
26	31	26.6234567901235	36.125	-9.50154320987654	4.37654320987654
27	36	26.0123456790123	35.9583333333333	-9.945987654321	9.98765432098767
28	34	27.0123456790123	36	-8.98765432098766	6.98765432098767
29	51	43.2530864197531	36	7.25308641975309	7.7469135802469
30	47	48.1234567901235	35.8333333333333	12.2901234567901	-1.12345679012346
31	31	36.8734567901235	35.375	1.49845679012345	-5.87345679012346
32	19	32.7530864197531	34.75	-1.99691358024692	-13.7530864197531
33	30	32.7854938271605	34.1666666666667	-1.38117283950618	-2.78549382716049
34	33	27.766975308642	33.625	-5.85802469135802	5.23302469135803
35	36	38.8364197530864	33.5833333333333	5.25308641975309	-2.83641975308642
36	40	41.5771604938272	34.0833333333333	7.49382716049383	-1.57716049382717
37	32	38.6743827160494	34.7916666666667	3.88271604938272	-6.67438271604938
38	25	26.4567901234568	35.9583333333333	-9.50154320987654	-1.45679012345679
39	28	27.3873456790123	37.3333333333333	-9.945987654321	0.612654320987666
40	29	29.2623456790123	38.25	-8.98765432098766	-0.262345679012341
41	55	46.3780864197531	39.125	7.25308641975309	8.62191358024691
42	55	52.7484567901235	40.4583333333333	12.2901234567901	2.25154320987654
43	40	43.9567901234568	42.4583333333333	1.49845679012345	-3.95679012345679
44	38	42.5864197530864	44.5833333333333	-1.99691358024692	-4.58641975308642
45	44	44.7438271604938	46.125	-1.38117283950618	-0.743827160493822
46	41	41.3086419753087	47.1666666666667	-5.85802469135802	-0.30864197530866
47	49	53.2947530864198	48.0416666666667	5.25308641975309	-4.29475308641975
48	59	56.5354938271605	49.0416666666667	7.49382716049383	2.46450617283951
49	61	54.4243827160494	50.5416666666667	3.88271604938272	6.57561728395062
50	47	43.2484567901235	52.75	-9.50154320987654	3.75154320987654
51	43	45.0123456790123	54.9583333333333	-9.945987654321	-2.01234567901234
52	39	47.679012345679	56.6666666666667	-8.98765432098766	-8.67901234567901
53	66	65.4614197530864	58.2083333333333	7.25308641975309	0.538580246913583
54	68	72.0817901234568	59.7916666666667	12.2901234567901	-4.08179012345679
55	63	62.9567901234568	61.4583333333333	1.49845679012345	0.0432098765432158
56	68	61.2947530864197	63.2916666666667	-1.99691358024692	6.70524691358025
57	67	63.6604938271605	65.0416666666667	-1.38117283950618	3.33950617283952
58	59	61.1836419753086	67.0416666666667	-5.85802469135802	-2.18364197530863
59	68	74.6697530864197	69.4166666666667	5.25308641975309	-6.66975308641975
60	78	79.4938271604938	72	7.49382716049383	-1.49382716049381
61	82	78.841049382716	74.9583333333333	3.88271604938272	3.15895061728395
62	70	68.4984567901235	78	-9.50154320987654	1.50154320987654
63	62	71.054012345679	81	-9.945987654321	-9.05401234567901
64	68	74.929012345679	83.9166666666667	-8.98765432098766	-6.929012345679
65	94	94.3364197530864	87.0833333333333	7.25308641975309	-0.336419753086403
66	102	102.623456790123	90.3333333333333	12.2901234567901	-0.623456790123456
67	100	94.9151234567901	93.4166666666667	1.49845679012345	5.08487654320987
68	104	94.3364197530864	96.3333333333333	-1.99691358024692	9.66358024691357
69	103	97.6604938271605	99.0416666666667	-1.38117283950618	5.33950617283952
70	93	96.016975308642	101.875	-5.85802469135802	-3.01697530864196
71	110	109.753086419753	104.5	5.25308641975309	0.246913580246911
72	114	114.702160493827	107.208333333333	7.49382716049383	-0.702160493827151
73	120	114.091049382716	110.208333333333	3.88271604938272	5.90895061728394
74	102	103.456790123457	112.958333333333	-9.50154320987654	-1.45679012345678
75	95	105.720679012346	115.666666666667	-9.945987654321	-10.7206790123457
76	103	109.637345679012	118.625	-8.98765432098766	-6.63734567901234
77	122	129.003086419753	121.75	7.25308641975309	-7.00308641975309
78	139	137.04012345679	124.75	12.2901234567901	1.95987654320987
79	135	128.706790123457	127.208333333333	1.49845679012345	6.29320987654323
80	135	127.336419753086	129.333333333333	-1.99691358024692	7.6635802469136
81	137	130.535493827160	131.916666666667	-1.38117283950618	6.46450617283952
82	130	128.808641975309	134.666666666667	-5.85802469135802	1.19135802469140
83	148	142.169753086420	136.916666666667	5.25308641975309	5.83024691358025
84	148	146.410493827160	138.916666666667	7.49382716049383	1.58950617283952
85	145	144.466049382716	140.583333333333	3.88271604938272	0.533950617283978
86	128	132.665123456790	142.166666666667	-9.50154320987654	-4.66512345679013
87	131	133.762345679012	143.708333333333	-9.945987654321	-2.76234567901236
88	133	136.137345679012	145.125	-8.98765432098766	-3.13734567901236
89	146	154.169753086420	146.916666666667	7.25308641975309	-8.16975308641975
90	163	160.998456790123	148.708333333333	12.2901234567901	2.00154320987656
91	151	151.623456790123	150.125	1.49845679012345	-0.623456790123413
92	157	149.669753086420	151.666666666667	-1.99691358024692	7.33024691358025
93	152	152.410493827161	153.791666666667	-1.38117283950618	-0.410493827160508
94	149	150.475308641975	156.333333333333	-5.85802469135802	-1.47530864197529
95	172	163.961419753086	158.708333333333	5.25308641975309	8.03858024691357
96	167	167.910493827160	160.416666666667	7.49382716049383	-0.91049382716048
97	160	165.799382716049	161.916666666667	3.88271604938272	-5.79938271604937
98	150	154.04012345679	163.541666666667	-9.50154320987654	-4.0401234567901
99	160	155.137345679012	165.083333333333	-9.945987654321	4.86265432098767
100	165	157.679012345679	166.666666666667	-8.98765432098766	7.32098765432096
101	171	175.669753086420	168.416666666667	7.25308641975309	-4.66975308641975
102	179	182.831790123457	170.541666666667	12.2901234567901	-3.83179012345681
103	171	174.41512345679	172.916666666667	1.49845679012345	-3.41512345679013
104	176	173.086419753086	175.083333333333	-1.99691358024692	2.91358024691357
105	170	175.785493827160	177.166666666667	-1.38117283950618	-5.78549382716048
106	169	173.475308641975	179.333333333333	-5.85802469135802	-4.47530864197529
107	194	186.753086419753	181.5	5.25308641975309	7.24691358024694
108	196	191.202160493827	183.708333333333	7.49382716049383	4.79783950617286
109	188	189.799382716049	185.916666666667	3.88271604938272	-1.79938271604934
110	174	178.66512345679	188.166666666667	-9.50154320987654	-4.66512345679013
111	186	180.679012345679	190.625	-9.945987654321	5.32098765432096
112	191	183.804012345679	192.791666666667	-8.98765432098766	7.19598765432102
113	197	201.711419753086	194.458333333333	7.25308641975309	-4.71141975308643
114	206	208.248456790123	195.958333333333	12.2901234567901	-2.24845679012341
115	197	NA	NA	1.49845679012345	NA
116	204	NA	NA	-1.99691358024692	NA
117	201	NA	NA	-1.38117283950618	NA
118	190	NA	NA	-5.85802469135802	NA
119	213	NA	NA	5.25308641975309	NA
120	213	NA	NA	7.49382716049383	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jul/30/t1280496181blpkby4hf9fb6c8/188zc1280496115.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jul/30/t1280496181blpkby4hf9fb6c8/188zc1280496115.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jul/30/t1280496181blpkby4hf9fb6c8/288zc1280496115.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jul/30/t1280496181blpkby4hf9fb6c8/288zc1280496115.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jul/30/t1280496181blpkby4hf9fb6c8/31zzf1280496115.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jul/30/t1280496181blpkby4hf9fb6c8/31zzf1280496115.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jul/30/t1280496181blpkby4hf9fb6c8/4t8yi1280496115.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jul/30/t1280496181blpkby4hf9fb6c8/4t8yi1280496115.ps (open in new window)

Parameters (Session):

par1 = 60 ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;

Parameters (R input):

par1 = additive ; par2 = 12 ; par3 = 0 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;

R code (references can be found in the software module):

par2 <- as.numeric(par2)

x <- ts(x,freq=par2)

m <- decompose(x,type=par1)

m$figure

bitmap(file='test1.png')

plot(m)

dev.off()

mylagmax <- length(x)/2

bitmap(file='test2.png')

op <- par(mfrow = c(2,2))

acf(as.numeric(x),lag.max = mylagmax,main='Observed')

acf(as.numeric(m$trend),na.action=na.pass,lag.max = mylagmax,main='Trend')

acf(as.numeric(m$seasonal),na.action=na.pass,lag.max = mylagmax,main='Seasonal')

acf(as.numeric(m$random),na.action=na.pass,lag.max = mylagmax,main='Random')

par(op)

dev.off()

bitmap(file='test3.png')

op <- par(mfrow = c(2,2))

spectrum(as.numeric(x),main='Observed')

spectrum(as.numeric(m$trend[!is.na(m$trend)]),main='Trend')

spectrum(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal')

spectrum(as.numeric(m$random[!is.na(m$random)]),main='Random')

par(op)

dev.off()

bitmap(file='test4.png')

op <- par(mfrow = c(2,2))

cpgram(as.numeric(x),main='Observed')

cpgram(as.numeric(m$trend[!is.na(m$trend)]),main='Trend')

cpgram(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal')

cpgram(as.numeric(m$random[!is.na(m$random)]),main='Random')

par(op)

dev.off()

load(file='createtable')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'Classical Decomposition by Moving Averages',6,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'t',header=TRUE)

a<-table.element(a,'Observations',header=TRUE)

a<-table.element(a,'Fit',header=TRUE)

a<-table.element(a,'Trend',header=TRUE)

a<-table.element(a,'Seasonal',header=TRUE)

a<-table.element(a,'Random',header=TRUE)

a<-table.row.end(a)

for (i in 1:length(m$trend)) {

a<-table.row.start(a)

a<-table.element(a,i,header=TRUE)

a<-table.element(a,x[i])

if (par1 == 'additive') a<-table.element(a,m$trend[i]+m$seasonal[i]) else a<-table.element(a,m$trend[i]*m$seasonal[i])

a<-table.element(a,m$trend[i])

a<-table.element(a,m$seasonal[i])

a<-table.element(a,m$random[i])

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable.tab')

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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