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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_cross.wasp
Title produced by softwareCross Correlation Function
Date of computationTue, 02 Dec 2008 09:26:37 -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/t1228235309642dvae4od28ndc.htm/, Retrieved Fri, 17 May 2024 03:42:23 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28037, Retrieved Fri, 17 May 2024 03:42:23 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact160

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F       [Cross Correlation Function] [] [2008-12-02 16:26:37] [76e580e81b2082744334eb1f6d9ccc3e] [Current]
Feedback Forum
2008-12-06 14:51:39 [Maarten Van Gucht] [reply] 
• De autocorrelatie functie meet hoe gegevens uit het verleden gebruikt kunnen worden om de toekomst te voorspellen.
• De cross correlatie functie meet hoe twee tijdsreeksen ( Xt en Yt) elkaar kunnen voorspellen.

de student heeft een goede berekening gedaan van zijn tijdreeks en een goede conclusie geschreven.
er moet nu nog wel gedifferentieerd worden, want we zien duidelijk dat er een patroon aanwezig is.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
105.3
103
103.8
103.4
105.8
101.4
97
94.3
96.6
97.1
95.7
96.9
97.4
95.3
93.6
91.5
93.1
91.7
94.3
93.9
90.9
88.3
91.3
91.7
92.4
92
95.6
95.8
96.4
99
107
109.7
116.2
115.9
113.8
112.6
113.7
115.9
110.3
111.3
113.4
108.2
104.8
106
110.9
115
118.4
121.4
128.8
131.7
141.7
142.9
139.4
134.7
125
113.6
111.5
108.5
112.3
116.6
115.5
120.1
132.9
128.1
129.3
132.5
131
124.9
120.8
122
122.1
127.4
135.2
137.3
135
136
138.4
134.7
138.4
133.9
133.6
141.2
151.8
155.4
156.6
161.6
160.7
156
159.5
168.7
169.9
169.9
185.9
190.8
195.8
211.9
Dataseries Y:
Download CSV
Histogram 
103.1
102.5
101.3
99.5
99.4
98.8
99.9
99.9
101.2
97.7
97
99.5
100.3
98.5
95.1
93.1
92.2
89
86.4
84.5
82.7
80.8
81.8
81.8
82.9
83.8
86.2
86.1
86.2
88.8
89.6
87.8
88.3
88.6
91
91.5
95.4
98.7
99.9
98.6
100.3
100.2
100.4
101.4
103
109.1
111.4
114.1
121.8
127.6
129.9
128
123.5
124
127.4
127.6
128.4
131.4
135.1
134
144.5
147.3
150.9
148.7
141.4
138.9
139.8
145.6
147.9
148.5
151.1
157.5
167.5
172.3
173.5
187.5
205.5
195.1
204.5
204.5
201.7
207
206.6
210.6
211.1
215
223.9
238.2
238.9
229.6
232.2
222.1
221.6
227.3
221
213.6

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'Gwilym Jenkins' @ 72.249.127.135

\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 & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28037&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28037&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28037&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'Gwilym Jenkins' @ 72.249.127.135






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)12
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])
-160.385507759687255
-150.404322395896884
-140.427677083493961
-130.453644987350511
-120.481606344346124
-110.50901810919301
-100.537277879657943
-90.561610300604371
-80.581736449163275
-70.605293374610261
-60.636513152966855
-50.670187713545022
-40.703089283564049
-30.743952037494834
-20.787254071537223
-10.834488829208724
00.890661317816725
10.881867588760631
20.867529566926654
30.84335505600798
40.821831286488408
50.800133415886628
60.76708312577515
70.73346041900747
80.69063011350139
90.646909942556987
100.612183725401537
110.581047830760801
120.550583886355772
130.519775576227819
140.488259720971733
150.451150030785147
160.417444167902581

\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) & 12 \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
-16 & 0.385507759687255 \tabularnewline
-15 & 0.404322395896884 \tabularnewline
-14 & 0.427677083493961 \tabularnewline
-13 & 0.453644987350511 \tabularnewline
-12 & 0.481606344346124 \tabularnewline
-11 & 0.50901810919301 \tabularnewline
-10 & 0.537277879657943 \tabularnewline
-9 & 0.561610300604371 \tabularnewline
-8 & 0.581736449163275 \tabularnewline
-7 & 0.605293374610261 \tabularnewline
-6 & 0.636513152966855 \tabularnewline
-5 & 0.670187713545022 \tabularnewline
-4 & 0.703089283564049 \tabularnewline
-3 & 0.743952037494834 \tabularnewline
-2 & 0.787254071537223 \tabularnewline
-1 & 0.834488829208724 \tabularnewline
0 & 0.890661317816725 \tabularnewline
1 & 0.881867588760631 \tabularnewline
2 & 0.867529566926654 \tabularnewline
3 & 0.84335505600798 \tabularnewline
4 & 0.821831286488408 \tabularnewline
5 & 0.800133415886628 \tabularnewline
6 & 0.76708312577515 \tabularnewline
7 & 0.73346041900747 \tabularnewline
8 & 0.69063011350139 \tabularnewline
9 & 0.646909942556987 \tabularnewline
10 & 0.612183725401537 \tabularnewline
11 & 0.581047830760801 \tabularnewline
12 & 0.550583886355772 \tabularnewline
13 & 0.519775576227819 \tabularnewline
14 & 0.488259720971733 \tabularnewline
15 & 0.451150030785147 \tabularnewline
16 & 0.417444167902581 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28037&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]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]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]-16[/C][C]0.385507759687255[/C][/ROW]
[ROW][C]-15[/C][C]0.404322395896884[/C][/ROW]
[ROW][C]-14[/C][C]0.427677083493961[/C][/ROW]
[ROW][C]-13[/C][C]0.453644987350511[/C][/ROW]
[ROW][C]-12[/C][C]0.481606344346124[/C][/ROW]
[ROW][C]-11[/C][C]0.50901810919301[/C][/ROW]
[ROW][C]-10[/C][C]0.537277879657943[/C][/ROW]
[ROW][C]-9[/C][C]0.561610300604371[/C][/ROW]
[ROW][C]-8[/C][C]0.581736449163275[/C][/ROW]
[ROW][C]-7[/C][C]0.605293374610261[/C][/ROW]
[ROW][C]-6[/C][C]0.636513152966855[/C][/ROW]
[ROW][C]-5[/C][C]0.670187713545022[/C][/ROW]
[ROW][C]-4[/C][C]0.703089283564049[/C][/ROW]
[ROW][C]-3[/C][C]0.743952037494834[/C][/ROW]
[ROW][C]-2[/C][C]0.787254071537223[/C][/ROW]
[ROW][C]-1[/C][C]0.834488829208724[/C][/ROW]
[ROW][C]0[/C][C]0.890661317816725[/C][/ROW]
[ROW][C]1[/C][C]0.881867588760631[/C][/ROW]
[ROW][C]2[/C][C]0.867529566926654[/C][/ROW]
[ROW][C]3[/C][C]0.84335505600798[/C][/ROW]
[ROW][C]4[/C][C]0.821831286488408[/C][/ROW]
[ROW][C]5[/C][C]0.800133415886628[/C][/ROW]
[ROW][C]6[/C][C]0.76708312577515[/C][/ROW]
[ROW][C]7[/C][C]0.73346041900747[/C][/ROW]
[ROW][C]8[/C][C]0.69063011350139[/C][/ROW]
[ROW][C]9[/C][C]0.646909942556987[/C][/ROW]
[ROW][C]10[/C][C]0.612183725401537[/C][/ROW]
[ROW][C]11[/C][C]0.581047830760801[/C][/ROW]
[ROW][C]12[/C][C]0.550583886355772[/C][/ROW]
[ROW][C]13[/C][C]0.519775576227819[/C][/ROW]
[ROW][C]14[/C][C]0.488259720971733[/C][/ROW]
[ROW][C]15[/C][C]0.451150030785147[/C][/ROW]
[ROW][C]16[/C][C]0.417444167902581[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28037&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28037&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)12
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])
-160.385507759687255
-150.404322395896884
-140.427677083493961
-130.453644987350511
-120.481606344346124
-110.50901810919301
-100.537277879657943
-90.561610300604371
-80.581736449163275
-70.605293374610261
-60.636513152966855
-50.670187713545022
-40.703089283564049
-30.743952037494834
-20.787254071537223
-10.834488829208724
00.890661317816725
10.881867588760631
20.867529566926654
30.84335505600798
40.821831286488408
50.800133415886628
60.76708312577515
70.73346041900747
80.69063011350139
90.646909942556987
100.612183725401537
110.581047830760801
120.550583886355772
130.519775576227819
140.488259720971733
150.451150030785147
160.417444167902581


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 12 ; par5 = 1 ; par6 = 0 ; par7 = 0 ;
Parameters (R input):
par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 12 ; 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')