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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_cross.wasp
Title produced by softwareCross Correlation Function
Date of computationTue, 02 Dec 2008 09:03:45 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Dec/02/t1228233969my20cz4oj708rkq.htm/, Retrieved Sat, 25 May 2024 12:25:31 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27999, Retrieved Sat, 25 May 2024 12:25:31 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact170

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Univariate Data Series] [Airline data] [2007-10-18 09:58:47] [42daae401fd3def69a25014f2252b4c2]
F RMPD    [Cross Correlation Function] [Workshop 7, Q8] [2008-12-02 16:03:45] [d300b7a0882cee7d84584ad37a3d4ede] [Current]
Feedback Forum
2008-12-06 09:57:10 [Loïque Verhasselt] [reply] 
Q8: We krijgen hier geen beredenering van de stappen die de student heeft overlopen om haar eigen tijdreeksen stationair te maken. Dit was echter wel de bedoeling.De student gebruikt hier alleen maar de uiteindelijke output in de cross correlatie functie.We bekijken dus eerst de differentiatie waarden uit het VRM (seasonal period op 12!).Als we dit aanpassen krijgen we de laagste variantie bij d=2 en D=0 bij suiker omdat er geen seizoenaliteit aanwezig is in de student zijn tijdreeks. Als we dit ook aanpassen bij jam krijgen we de laagste variantie bij d=1 en D=0 opnieuw omdat er geen seizoenaliteit is. Om de lambda te vinden gebruiken we het Standaard Deviatie Mean Plot die ons voor beide tijdreeksen een correcte lambda zal geven. Na dit alles kunnen we deze 6 parameters invullen in de cross correlatie functie en krijgen we een stationair model.Deze bespreken we. Deze is stationair omdat de trend en de mogelijke seizoenaliteit gezuiverd is met een constante spreiding.
2008-12-06 11:04:39 [Britt Severijns] [reply] 
Je geeft hier enkel een grafiek maar geen uitleg hoe je hier op komt. We gaan hier weer proberen om de grafiek stationair te maken. We moeten dus lambda,d en D zoeken. Lambda kunnen we vinden door de standard deviation mean plot. De andere twee, d en D, kunnen we vinden met de autocorrelation funtie,variance reduction matrix en de spectraalanalyse.
2008-12-06 13:44:24 [Nicolaj Wuyts] [reply] 
De student geeft de d, D en lamba waarden niet weer. De d en D waarden zijn zowel voor de x- als Y-serie gelijk aan 1. Ook de lamba is voor beiden reeksen gelijk aan 1. Aan de grafiek kunnen we nu zien dat er geen enkele significante waarde meer overblijft.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
101.02
100.67
100.47
100.38
100.33
100.34
100.37
100.39
100.21
100.21
100.22
100.28
100.25
100.25
100.21
100.16
100.18
100.1
99.96
99.88
99.88
99.86
99.84
99.8
99.82
99.81
99.92
100.03
99.99
100.02
100.01
100.13
100.33
100.13
99.96
100.05
99.83
99.8
100.01
100.1
100.13
100.16
100.41
101.34
101.65
101.85
102.07
102.12
102.14
102.21
102.28
102.19
102.33
102.54
102.44
102.78
102.9
103.08
102.77
102.65
102.71
103.29
102.86
103.45
103.72
103.65
103.83
104.45
105.14
105.07
105.31
105.19
105.3
105.02
105.17
105.28
105.45
105.38
105.8
105.96
105.08
105.11
105.61
105.5
Dataseries Y:
Download CSV
Histogram 
103.68
103.64
103.37
104.3
104.15
104.09
104.21
104.27
104
103.36
104.2
104.12
103.79
104.65
103.84
103.98
103.83
104.34
103.76
103.57
103.06
103.06
102.6
103.41
103.15
103.33
103.96
104.91
104.23
103.68
104.16
104.49
104.23
104.21
103.74
103.96
104.02
104.15
103.74
103.23
103.69
103.46
102.14
102.39
102.19
102.02
102.64
103.52
103.32
103.65
104.25
101.74
102.08
101.35
102.79
102.21
101.78
101.25
101.8
103
104.17
104.08
105.24
104.72
104.77
104.39
104.14
105.15
105.07
104.54
106.03
107.24
108.2
109.15
110.1
109.48
109.96
110.13
110.53
110.82
110.06
110.05
109.49
109.95

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'George Udny Yule' @ 72.249.76.132

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 1 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27999&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27999&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27999&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'George Udny Yule' @ 72.249.76.132






Cross Correlation Function
ParameterValue
Box-Cox transformation parameter (lambda) of X series1
Degree of non-seasonal differencing (d) of X series1
Degree of seasonal differencing (D) of X series1
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series1
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-15-0.00811856233109424
-140.00811745813351938
-13-0.102432093142714
-12-0.139940857586646
-110.072640470251414
-100.0792860752763264
-9-0.0226067504291713
-8-0.156006645493367
-70.103267283732203
-60.0741468529130668
-5-0.179556922861117
-40.0728526461319216
-30.081068318503672
-20.170328463027516
-10.0309430572854006
00.0688371713976095
10.0654081838330542
2-0.208643135244755
30.0916954648686402
4-0.108582799151465
50.0105521661342105
6-0.129741838655056
70.148342127767274
8-0.149467496624431
90.0658619766666223
10-0.237206827487500
110.0370448288183098
12-0.0698301012867484
13-0.0917946682089323
140.247426927780811
15-0.111238655087579

\begin{tabular}{lllllllll}
\hline
Cross Correlation Function \tabularnewline
Parameter & Value \tabularnewline
Box-Cox transformation parameter (lambda) of X series & 1 \tabularnewline
Degree of non-seasonal differencing (d) of X series & 1 \tabularnewline
Degree of seasonal differencing (D) of X series & 1 \tabularnewline
Seasonal Period (s) & 12 \tabularnewline
Box-Cox transformation parameter (lambda) of Y series & 1 \tabularnewline
Degree of non-seasonal differencing (d) of Y series & 1 \tabularnewline
Degree of seasonal differencing (D) of Y series & 1 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-15 & -0.00811856233109424 \tabularnewline
-14 & 0.00811745813351938 \tabularnewline
-13 & -0.102432093142714 \tabularnewline
-12 & -0.139940857586646 \tabularnewline
-11 & 0.072640470251414 \tabularnewline
-10 & 0.0792860752763264 \tabularnewline
-9 & -0.0226067504291713 \tabularnewline
-8 & -0.156006645493367 \tabularnewline
-7 & 0.103267283732203 \tabularnewline
-6 & 0.0741468529130668 \tabularnewline
-5 & -0.179556922861117 \tabularnewline
-4 & 0.0728526461319216 \tabularnewline
-3 & 0.081068318503672 \tabularnewline
-2 & 0.170328463027516 \tabularnewline
-1 & 0.0309430572854006 \tabularnewline
0 & 0.0688371713976095 \tabularnewline
1 & 0.0654081838330542 \tabularnewline
2 & -0.208643135244755 \tabularnewline
3 & 0.0916954648686402 \tabularnewline
4 & -0.108582799151465 \tabularnewline
5 & 0.0105521661342105 \tabularnewline
6 & -0.129741838655056 \tabularnewline
7 & 0.148342127767274 \tabularnewline
8 & -0.149467496624431 \tabularnewline
9 & 0.0658619766666223 \tabularnewline
10 & -0.237206827487500 \tabularnewline
11 & 0.0370448288183098 \tabularnewline
12 & -0.0698301012867484 \tabularnewline
13 & -0.0917946682089323 \tabularnewline
14 & 0.247426927780811 \tabularnewline
15 & -0.111238655087579 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27999&T=1

[TABLE]
[ROW][C]Cross Correlation Function[/C][/ROW]
[ROW][C]Parameter[/C][C]Value[/C][/ROW]
[ROW][C]Box-Cox transformation parameter (lambda) of X series[/C][C]1[/C][/ROW]
[ROW][C]Degree of non-seasonal differencing (d) of X series[/C][C]1[/C][/ROW]
[ROW][C]Degree of seasonal differencing (D) of X series[/C][C]1[/C][/ROW]
[ROW][C]Seasonal Period (s)[/C][C]12[/C][/ROW]
[ROW][C]Box-Cox transformation parameter (lambda) of Y series[/C][C]1[/C][/ROW]
[ROW][C]Degree of non-seasonal differencing (d) of Y series[/C][C]1[/C][/ROW]
[ROW][C]Degree of seasonal differencing (D) of Y series[/C][C]1[/C][/ROW]
[ROW][C]k[/C][C]rho(Y[t],X[t+k])[/C][/ROW]
[ROW][C]-15[/C][C]-0.00811856233109424[/C][/ROW]
[ROW][C]-14[/C][C]0.00811745813351938[/C][/ROW]
[ROW][C]-13[/C][C]-0.102432093142714[/C][/ROW]
[ROW][C]-12[/C][C]-0.139940857586646[/C][/ROW]
[ROW][C]-11[/C][C]0.072640470251414[/C][/ROW]
[ROW][C]-10[/C][C]0.0792860752763264[/C][/ROW]
[ROW][C]-9[/C][C]-0.0226067504291713[/C][/ROW]
[ROW][C]-8[/C][C]-0.156006645493367[/C][/ROW]
[ROW][C]-7[/C][C]0.103267283732203[/C][/ROW]
[ROW][C]-6[/C][C]0.0741468529130668[/C][/ROW]
[ROW][C]-5[/C][C]-0.179556922861117[/C][/ROW]
[ROW][C]-4[/C][C]0.0728526461319216[/C][/ROW]
[ROW][C]-3[/C][C]0.081068318503672[/C][/ROW]
[ROW][C]-2[/C][C]0.170328463027516[/C][/ROW]
[ROW][C]-1[/C][C]0.0309430572854006[/C][/ROW]
[ROW][C]0[/C][C]0.0688371713976095[/C][/ROW]
[ROW][C]1[/C][C]0.0654081838330542[/C][/ROW]
[ROW][C]2[/C][C]-0.208643135244755[/C][/ROW]
[ROW][C]3[/C][C]0.0916954648686402[/C][/ROW]
[ROW][C]4[/C][C]-0.108582799151465[/C][/ROW]
[ROW][C]5[/C][C]0.0105521661342105[/C][/ROW]
[ROW][C]6[/C][C]-0.129741838655056[/C][/ROW]
[ROW][C]7[/C][C]0.148342127767274[/C][/ROW]
[ROW][C]8[/C][C]-0.149467496624431[/C][/ROW]
[ROW][C]9[/C][C]0.0658619766666223[/C][/ROW]
[ROW][C]10[/C][C]-0.237206827487500[/C][/ROW]
[ROW][C]11[/C][C]0.0370448288183098[/C][/ROW]
[ROW][C]12[/C][C]-0.0698301012867484[/C][/ROW]
[ROW][C]13[/C][C]-0.0917946682089323[/C][/ROW]
[ROW][C]14[/C][C]0.247426927780811[/C][/ROW]
[ROW][C]15[/C][C]-0.111238655087579[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27999&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27999&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Cross Correlation Function
ParameterValue
Box-Cox transformation parameter (lambda) of X series1
Degree of non-seasonal differencing (d) of X series1
Degree of seasonal differencing (D) of X series1
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series1
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-15-0.00811856233109424
-140.00811745813351938
-13-0.102432093142714
-12-0.139940857586646
-110.072640470251414
-100.0792860752763264
-9-0.0226067504291713
-8-0.156006645493367
-70.103267283732203
-60.0741468529130668
-5-0.179556922861117
-40.0728526461319216
-30.081068318503672
-20.170328463027516
-10.0309430572854006
00.0688371713976095
10.0654081838330542
2-0.208643135244755
30.0916954648686402
4-0.108582799151465
50.0105521661342105
6-0.129741838655056
70.148342127767274
8-0.149467496624431
90.0658619766666223
10-0.237206827487500
110.0370448288183098
12-0.0698301012867484
13-0.0917946682089323
140.247426927780811
15-0.111238655087579


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 1 ; par3 = 1 ; par4 = 12 ; par5 = 1 ; par6 = 1 ; par7 = 1 ;
Parameters (R input):
par1 = 1 ; par2 = 1 ; par3 = 1 ; par4 = 12 ; par5 = 1 ; par6 = 1 ; par7 = 1 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
par2 <- as.numeric(par2)
par3 <- as.numeric(par3)
par4 <- as.numeric(par4)
par5 <- as.numeric(par5)
par6 <- as.numeric(par6)
par7 <- as.numeric(par7)
if (par1 == 0) {
x <- log(x)
} else {
x <- (x ^ par1 - 1) / par1
}
if (par5 == 0) {
y <- log(y)
} else {
y <- (y ^ par5 - 1) / par5
}
if (par2 > 0) x <- diff(x,lag=1,difference=par2)
if (par6 > 0) y <- diff(y,lag=1,difference=par6)
if (par3 > 0) x <- diff(x,lag=par4,difference=par3)
if (par7 > 0) y <- diff(y,lag=par4,difference=par7)
x
y
bitmap(file='test1.png')
(r <- ccf(x,y,main='Cross Correlation Function',ylab='CCF',xlab='Lag (k)'))
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Cross Correlation Function',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'Value',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Box-Cox transformation parameter (lambda) of X series',header=TRUE)
a<-table.element(a,par1)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Degree of non-seasonal differencing (d) of X series',header=TRUE)
a<-table.element(a,par2)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Degree of seasonal differencing (D) of X series',header=TRUE)
a<-table.element(a,par3)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Seasonal Period (s)',header=TRUE)
a<-table.element(a,par4)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Box-Cox transformation parameter (lambda) of Y series',header=TRUE)
a<-table.element(a,par5)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Degree of non-seasonal differencing (d) of Y series',header=TRUE)
a<-table.element(a,par6)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Degree of seasonal differencing (D) of Y series',header=TRUE)
a<-table.element(a,par7)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'k',header=TRUE)
a<-table.element(a,'rho(Y[t],X[t+k])',header=TRUE)
a<-table.row.end(a)
mylength <- length(r$acf)
myhalf <- floor((mylength-1)/2)
for (i in 1:mylength) {
a<-table.row.start(a)
a<-table.element(a,i-myhalf-1,header=TRUE)
a<-table.element(a,r$acf[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')