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Data:
1025.5 691.2 971.6 926 997.1 964.9 860 948 951.4 827.3 994 944.5 976.2 668.8 939.9 1096.1 977.7 1096.9 1060.8 1121.4 1190.9 1177.9 1108.1 1045.6 1263.9 911 1175.9 1091.3 1027.7 1081.7 879.7 955.5 1037.9 959.9 931.8 1062.2 1077.2 668.4 954.3 797.2 829.2 957.3 844.2 893.6 1132 898.8 1064 1279.7 1382.5 824.1 1304.1 1253.5 1136.3 1414.7 1293.2 1325.7 1463.8 1244.2 1573.6 1327.3 1418.5 1042.2 1384.8 1474.8 1556.5 1466.2 1221.7 1279.7 1348.4 1189.8 1296.6 1417.6 1513.9 1006.1 1202.8 1258.8 1211.5 1283.3 1332.3 1374.3 1406.1 1419.1 1554.4 1499.8 1609.6 1033.9 1550.5 1491.4 1368.9 1537.1 1492.3 1504.1 1301.2 1344.2 1319.1 1420.3 1582.9 1002.6 1559.1 1462.7 1414.8 1537.5 1455.9 1619.9 1667.2 1488.9 1442.5 1779.6 1801.9 1233.4 1581.1 1515 1439.2 1585.8 1488.8 1601.3 1646.8 1630.2 1720.7 2013.5 2051.2 1404.7 2015.9 1544.1 1816.6 1773.4 1577.4 1709.8 1810.2 1520.5 1798.6 1666.8 1730.4 1147.8 1777 1700 1907.4 1745.8 1771.6 1790.2 1958.7 1560.4 1752.1 2011.6 2082.8 1616.4 1846.1 1824.9 1711.3 1805 1737.6 1939.6 1711.4 1964.8 1864.4 1980.7 2226.7 1433.3 1960.7
Sample Range:
(leave blank to include all observations)
From:
To:
Number of time lags
60
Default
5
6
7
8
9
10
11
12
24
36
48
60
Box-Cox transformation parameter (Lambda)
0.3
1
-2.0
-1.9
-1.8
-1.7
-1.6
-1.5
-1.4
-1.3
-1.2
-1.1
-1.0
-0.9
-0.8
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
2.0
Degree of non-seasonal differencing (d)
1
0
1
2
Degree of seasonal differencing (D)
1
0
1
2
Seasonality
12
12
1
2
3
4
6
12
CI type
White Noise
MA
Confidence Interval
Use logarithms with this base
(overrules the Box-Cox lambda parameter)
(?)
Chart options
R Code
if (par1 == 'Default') { par1 = 10*log10(length(x)) } else { par1 <- as.numeric(par1) } par2 <- as.numeric(par2) par3 <- as.numeric(par3) par4 <- as.numeric(par4) par5 <- as.numeric(par5) if (par2 == 0) { x <- log(x) } else { x <- (x ^ par2 - 1) / par2 } if (par3 > 0) x <- diff(x,lag=1,difference=par3) if (par4 > 0) x <- diff(x,lag=par5,difference=par4) bitmap(file='pic1.png') racf <- acf(x,par1,main='Autocorrelation',xlab='lags',ylab='ACF') dev.off() bitmap(file='pic2.png') rpacf <- pacf(x,par1,main='Partial Autocorrelation',xlab='lags',ylab='PACF') dev.off() (myacf <- c(racf$acf)) (mypacf <- c(rpacf$acf)) lengthx <- length(x) sqrtn <- sqrt(lengthx) load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Autocorrelation Function',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Time lag k',header=TRUE) a<-table.element(a,hyperlink('http://www.xycoon.com/basics.htm','ACF(k)','click here for more information about the Autocorrelation Function'),header=TRUE) a<-table.element(a,'T-STAT',header=TRUE) a<-table.element(a,'P-value',header=TRUE) a<-table.row.end(a) for (i in 2:(par1+1)) { a<-table.row.start(a) a<-table.element(a,i-1,header=TRUE) a<-table.element(a,round(myacf[i],6)) mytstat <- myacf[i]*sqrtn a<-table.element(a,round(mytstat,4)) a<-table.element(a,round(1-pt(abs(mytstat),lengthx),6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Partial Autocorrelation Function',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Time lag k',header=TRUE) a<-table.element(a,hyperlink('http://www.xycoon.com/basics.htm','PACF(k)','click here for more information about the Partial Autocorrelation Function'),header=TRUE) a<-table.element(a,'T-STAT',header=TRUE) a<-table.element(a,'P-value',header=TRUE) a<-table.row.end(a) for (i in 1:par1) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,round(mypacf[i],6)) mytstat <- mypacf[i]*sqrtn a<-table.element(a,round(mytstat,4)) a<-table.element(a,round(1-pt(abs(mytstat),lengthx),6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable1.tab')
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Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
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