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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 09:48:44 -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/t1228150671qnbwta8pixdzrq0.htm/, Retrieved Sun, 05 May 2024 16:29:26 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=26992, Retrieved Sun, 05 May 2024 16:29:26 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact212

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] [Q6] [2008-12-01 16:48:44] [7ed4ec9f8cdf7df79ef87b9dc09dff20] [Current]
Feedback Forum
2008-12-03 10:12:04 [Romina Machiels] [reply] 
Er werd wel een berekening gemaakt maar meer niet.
De bedoeling was dat je de ACF, VRM en Spectrum een aantal keer ging reproduceren. Bij ACF eerst met d=0 en D=0. Dan d=1 en D=0, hier zag je dat de langetermijntrend eruit werd gehaald. Dan nog een keer met d=1 en D=1, hierdood werd de seizonaliteit verwijderd.
dit zelfde moest bij VRM en Spectrum ook gedaan worden.
De conlusie is dan dat de tijdreeks stationair wordt bij d=1 en D=1, want dan verdwijnen de langetremijntrend en de seizonaliteit.
2008-12-08 18:41:01 [Jeroen Aerts] [reply] 
Het antwoord op de vraag is foutief, ook de berekening is niet correct uigevoerd. Omdat er in de berekening niet met 12 maanden werd gewerkt, dan had je wel de seizoenaliteit gevonden
Je had dus niet-seizoenaal en seizoenaal moeten differentiëren om de kleinste variantie te bekomen. Als je dan een nieuwe ACF produceert, kom je tot de juiste oplossing.

De juiste berekening vind je hier: Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/08/t122876154458ei2qg544wp031.htm, Retrieved Mon, 08 Dec 2008 18:39:09 +0000


De trend lijkt nu volledig verdwenen te zijn en van seizoenaliteit is er geen sprake meer.

2008-12-08 19:01:56 [Yara Van Overstraeten] [reply] 
Inderdaad Jeroen, goed opgemerkt dat de Seasonal Period (s) hier inderdaad op 12 had moeten staan om de juiste berekening te maken. Dan had je kunnen besluiten dat door het doorvoeren van transformaties de lange termijn trend en seizonaliteit verwijderd wordt.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
99.2
99
100
111.6
122.2
117.6
121.1
136
154.2
153.6
158.5
140.6
136.2
168
154.3
149
165.5
Dataseries Y:
Download CSV
Histogram 
96.7
98.1
100
104.9
104.9
109.5
110.8
112.3
109.3
105.3
101.7
95.4
96.4
97.6
102.4
101.6
103.8

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 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=26992&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]2 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=26992&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26992&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 time2 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])
-90.236006556160983
-80.053221581239011
-7-0.169833066960884
-6-0.336047226350378
-5-0.517192563302102
-4-0.573470967646541
-3-0.5885180954614
-2-0.452605867411524
-1-0.202374044224987
00.0617694041009443
10.144479690299472
20.166031833197589
30.159983697754745
40.179177574128377
50.232674124098104
60.292424183865019
70.258003898887469
80.215196574475449
90.171680328730759

\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
-9 & 0.236006556160983 \tabularnewline
-8 & 0.053221581239011 \tabularnewline
-7 & -0.169833066960884 \tabularnewline
-6 & -0.336047226350378 \tabularnewline
-5 & -0.517192563302102 \tabularnewline
-4 & -0.573470967646541 \tabularnewline
-3 & -0.5885180954614 \tabularnewline
-2 & -0.452605867411524 \tabularnewline
-1 & -0.202374044224987 \tabularnewline
0 & 0.0617694041009443 \tabularnewline
1 & 0.144479690299472 \tabularnewline
2 & 0.166031833197589 \tabularnewline
3 & 0.159983697754745 \tabularnewline
4 & 0.179177574128377 \tabularnewline
5 & 0.232674124098104 \tabularnewline
6 & 0.292424183865019 \tabularnewline
7 & 0.258003898887469 \tabularnewline
8 & 0.215196574475449 \tabularnewline
9 & 0.171680328730759 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26992&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]-9[/C][C]0.236006556160983[/C][/ROW]
[ROW][C]-8[/C][C]0.053221581239011[/C][/ROW]
[ROW][C]-7[/C][C]-0.169833066960884[/C][/ROW]
[ROW][C]-6[/C][C]-0.336047226350378[/C][/ROW]
[ROW][C]-5[/C][C]-0.517192563302102[/C][/ROW]
[ROW][C]-4[/C][C]-0.573470967646541[/C][/ROW]
[ROW][C]-3[/C][C]-0.5885180954614[/C][/ROW]
[ROW][C]-2[/C][C]-0.452605867411524[/C][/ROW]
[ROW][C]-1[/C][C]-0.202374044224987[/C][/ROW]
[ROW][C]0[/C][C]0.0617694041009443[/C][/ROW]
[ROW][C]1[/C][C]0.144479690299472[/C][/ROW]
[ROW][C]2[/C][C]0.166031833197589[/C][/ROW]
[ROW][C]3[/C][C]0.159983697754745[/C][/ROW]
[ROW][C]4[/C][C]0.179177574128377[/C][/ROW]
[ROW][C]5[/C][C]0.232674124098104[/C][/ROW]
[ROW][C]6[/C][C]0.292424183865019[/C][/ROW]
[ROW][C]7[/C][C]0.258003898887469[/C][/ROW]
[ROW][C]8[/C][C]0.215196574475449[/C][/ROW]
[ROW][C]9[/C][C]0.171680328730759[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26992&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26992&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])
-90.236006556160983
-80.053221581239011
-7-0.169833066960884
-6-0.336047226350378
-5-0.517192563302102
-4-0.573470967646541
-3-0.5885180954614
-2-0.452605867411524
-1-0.202374044224987
00.0617694041009443
10.144479690299472
20.166031833197589
30.159983697754745
40.179177574128377
50.232674124098104
60.292424183865019
70.258003898887469
80.215196574475449
90.171680328730759


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')