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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 computationMon, 01 Dec 2008 11:36:52 -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/01/t1228156680juhriqp2rkry3y8.htm/, Retrieved Sun, 05 May 2024 15:51:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27116, Retrieved Sun, 05 May 2024 15:51:28 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact276

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Spectral Analysis] [Spectral analyse ...] [2007-11-22 13:17:53] [ced1562ed3c62c3bc1f3b66b8f83b537]
- R PD  [Spectral Analysis] [WS 9] [2007-11-26 17:48:35] [74be16979710d4c4e7c6647856088456]
F    D    [Spectral Analysis] [Q4 derde link] [2008-12-01 18:24:30] [077ffec662d24c06be4c491541a44245]
F RMPD        [Cross Correlation Function] [Q7] [2008-12-01 18:36:52] [3817f5e632a8bfeb1be7b5e8c86bd450] [Current]
-   P           [Cross Correlation Function] [Assesment] [2008-12-08 19:53:25] [988ab43f527fc78aae41c84649095267]
-   P           [Cross Correlation Function] [Assesment] [2008-12-08 19:55:50] [988ab43f527fc78aae41c84649095267]
Feedback Forum
2008-12-08 18:58:55 [Nathalie Boden] [reply] 
Antwoord op Q7,Q8 en Q9 = we gaan met twee willekeurige tijdreeksen werken. We doen een opeenvolging van alle correlatiecoëfficiënten. We gaan hier de correlatie berekenen tussen Yt en een andere tijdreeks bv. Yt-2. We gaan hierbij ook kijken in welke mate een variabele voorspelt kan worden. Op de grafiek hebben we niet te maken met een trend maar wel wat het verband is tussen x en y. Wanneer de waarden binnen het betrouwbaarheidsinterval vallen is het niet significant. Zoals dit in mijn tijdreeks het geval is. We gaan ook bij d=1 en D=1 in de parameter invullen. De bedoeling is dat we de tijdreeksen stationair gaan maken en de trend doen verdwijnen. Het is belangrijk om een goede differentiatie te kiezen.
2008-12-08 19:57:51 [Stijn Loomans] [reply] 
Je hebt alleen q7 gemaakt , en niet meer q8 en Q9 wat op dezelfde tijdreeksen slaagt.De uiteindelijk bedoeling van deze 3 vragen is dat we de tijdreeksen gaan stationair maken door de D en d, lambda tevinden.

Dus we gaan de correlatie bereken tussen Yt(tijdreeks1) en en xT(tijdreeks2)
Op de grafiek kan je hier niet echt een dalende trend aflezen. Wel een soort piramide vorm wat duid op seizonaliteit. Je waarden vallen ook buiten de betrouwbaarheidsinterval wat dus betekend dat het siginificant verschillend is.
Q8
Je moet de Seasonal Period op 12 zetten om te beginnen(want je tijdreeksen zijn in maanden)
Zo ga je bij q8 verder met Dx=1 en DY=1 te zetten want seizonaliteit kan zich voor gaan doen.
http://www.freestatistics.org/blog/date/2008/Dec/08/t1228766179h6zvxizl1r60bir.htm

Als je naar deze grafiek gaat zien zie je dat ie Stationair is geworden. Dus moet mag je kleine d op 0 laten . Staan
q9
Nu moet je alle gevonden waarden gaan toepassen op je time series en dan krijgje ze in stationaire vorm zoals in de link hierboven is weergegeven




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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
12300.00
12092.80
12380.80
12196.90
9455.00
13168.00
13427.90
11980.50
11884.80
11691.70
12233.80
14341.40
13130.70
12421.10
14285.80
12864.60
11160.20
14316.20
14388.70
14013.90
13419.00
12769.60
13315.50
15332.90
14243.00
13824.40
14962.90
13202.90
12199.00
15508.90
14199.80
15169.60
14058.00
13786.20
14147.90
16541.70
13587.50
15582.40
15802.80
14130.50
12923.20
15612.20
16033.70
16036.60
14037.80
15330.60
15038.30
17401.80
14992.50
16043.70
16929.60
15921.30
14417.20
15961.00
17851.90
16483.90
14215.50
17429.70
17839.50
17629.20
Dataseries Y:
Download CSV
Histogram 
15370.60
14956.90
15469.70
15101.80
11703.70
16283.60
16726.50
14968.90
14861.00
14583.30
15305.80
17903.90
16379.40
15420.30
17870.50
15912.80
13866.50
17823.20
17872.00
17420.40
16704.40
15991.20
16583.60
19123.50
17838.70
17209.40
18586.50
16258.10
15141.60
19202.10
17746.50
19090.10
18040.30
17515.50
17751.80
21072.40
17170.00
19439.50
19795.40
17574.90
16165.40
19464.60
19932.10
19961.20
17343.40
18924.20
18574.10
21350.60
18594.60
19823.10
20844.40
19640.20
17735.40
19813.60
22238.50
20682.20
17818.60
21872.10
22117.00
21865.90

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\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 & 3 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27116&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27116&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27116&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 time3 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001






Cross Correlation Function
ParameterValue
Box-Cox transformation parameter (lambda) of X series1
Degree of non-seasonal differencing (d) of X series0
Degree of seasonal differencing (D) of X series0
Seasonal Period (s)1
Box-Cox transformation parameter (lambda) of Y series1
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-140.0851570260788024
-130.162167259508440
-120.475904192009455
-110.242222271539631
-100.169096441286655
-90.321168682508674
-80.421333330248397
-70.349650231207287
-60.470901562271061
-50.430034421701228
-40.560898591917675
-30.50578264808746
-20.407208664836876
-10.565770747792416
00.997734994643543
10.563433518035905
20.411167690537476
30.510888933553843
40.564415963562821
50.433625078618267
60.466608209927658
70.344462662107657
80.416118496131979
90.321313106575043
100.173990715696836
110.249451782646385
120.48282895196177
130.168252745983479
140.0957926400437818

\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 & 0 \tabularnewline
Degree of seasonal differencing (D) of X series & 0 \tabularnewline
Seasonal Period (s) & 1 \tabularnewline
Box-Cox transformation parameter (lambda) of Y series & 1 \tabularnewline
Degree of non-seasonal differencing (d) of Y series & 0 \tabularnewline
Degree of seasonal differencing (D) of Y series & 0 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-14 & 0.0851570260788024 \tabularnewline
-13 & 0.162167259508440 \tabularnewline
-12 & 0.475904192009455 \tabularnewline
-11 & 0.242222271539631 \tabularnewline
-10 & 0.169096441286655 \tabularnewline
-9 & 0.321168682508674 \tabularnewline
-8 & 0.421333330248397 \tabularnewline
-7 & 0.349650231207287 \tabularnewline
-6 & 0.470901562271061 \tabularnewline
-5 & 0.430034421701228 \tabularnewline
-4 & 0.560898591917675 \tabularnewline
-3 & 0.50578264808746 \tabularnewline
-2 & 0.407208664836876 \tabularnewline
-1 & 0.565770747792416 \tabularnewline
0 & 0.997734994643543 \tabularnewline
1 & 0.563433518035905 \tabularnewline
2 & 0.411167690537476 \tabularnewline
3 & 0.510888933553843 \tabularnewline
4 & 0.564415963562821 \tabularnewline
5 & 0.433625078618267 \tabularnewline
6 & 0.466608209927658 \tabularnewline
7 & 0.344462662107657 \tabularnewline
8 & 0.416118496131979 \tabularnewline
9 & 0.321313106575043 \tabularnewline
10 & 0.173990715696836 \tabularnewline
11 & 0.249451782646385 \tabularnewline
12 & 0.48282895196177 \tabularnewline
13 & 0.168252745983479 \tabularnewline
14 & 0.0957926400437818 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27116&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]0[/C][/ROW]
[ROW][C]Degree of seasonal differencing (D) of X series[/C][C]0[/C][/ROW]
[ROW][C]Seasonal Period (s)[/C][C]1[/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]0[/C][/ROW]
[ROW][C]Degree of seasonal differencing (D) of Y series[/C][C]0[/C][/ROW]
[ROW][C]k[/C][C]rho(Y[t],X[t+k])[/C][/ROW]
[ROW][C]-14[/C][C]0.0851570260788024[/C][/ROW]
[ROW][C]-13[/C][C]0.162167259508440[/C][/ROW]
[ROW][C]-12[/C][C]0.475904192009455[/C][/ROW]
[ROW][C]-11[/C][C]0.242222271539631[/C][/ROW]
[ROW][C]-10[/C][C]0.169096441286655[/C][/ROW]
[ROW][C]-9[/C][C]0.321168682508674[/C][/ROW]
[ROW][C]-8[/C][C]0.421333330248397[/C][/ROW]
[ROW][C]-7[/C][C]0.349650231207287[/C][/ROW]
[ROW][C]-6[/C][C]0.470901562271061[/C][/ROW]
[ROW][C]-5[/C][C]0.430034421701228[/C][/ROW]
[ROW][C]-4[/C][C]0.560898591917675[/C][/ROW]
[ROW][C]-3[/C][C]0.50578264808746[/C][/ROW]
[ROW][C]-2[/C][C]0.407208664836876[/C][/ROW]
[ROW][C]-1[/C][C]0.565770747792416[/C][/ROW]
[ROW][C]0[/C][C]0.997734994643543[/C][/ROW]
[ROW][C]1[/C][C]0.563433518035905[/C][/ROW]
[ROW][C]2[/C][C]0.411167690537476[/C][/ROW]
[ROW][C]3[/C][C]0.510888933553843[/C][/ROW]
[ROW][C]4[/C][C]0.564415963562821[/C][/ROW]
[ROW][C]5[/C][C]0.433625078618267[/C][/ROW]
[ROW][C]6[/C][C]0.466608209927658[/C][/ROW]
[ROW][C]7[/C][C]0.344462662107657[/C][/ROW]
[ROW][C]8[/C][C]0.416118496131979[/C][/ROW]
[ROW][C]9[/C][C]0.321313106575043[/C][/ROW]
[ROW][C]10[/C][C]0.173990715696836[/C][/ROW]
[ROW][C]11[/C][C]0.249451782646385[/C][/ROW]
[ROW][C]12[/C][C]0.48282895196177[/C][/ROW]
[ROW][C]13[/C][C]0.168252745983479[/C][/ROW]
[ROW][C]14[/C][C]0.0957926400437818[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27116&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27116&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 series0
Degree of seasonal differencing (D) of X series0
Seasonal Period (s)1
Box-Cox transformation parameter (lambda) of Y series1
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-140.0851570260788024
-130.162167259508440
-120.475904192009455
-110.242222271539631
-100.169096441286655
-90.321168682508674
-80.421333330248397
-70.349650231207287
-60.470901562271061
-50.430034421701228
-40.560898591917675
-30.50578264808746
-20.407208664836876
-10.565770747792416
00.997734994643543
10.563433518035905
20.411167690537476
30.510888933553843
40.564415963562821
50.433625078618267
60.466608209927658
70.344462662107657
80.416118496131979
90.321313106575043
100.173990715696836
110.249451782646385
120.48282895196177
130.168252745983479
140.0957926400437818


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 1 ; par5 = 1 ; par6 = 0 ; par7 = 0 ;
Parameters (R input):
par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 1 ; par5 = 1 ; par6 = 0 ; par7 = 0 ;
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')