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Data:
353.4 329.08 331.89 339.94 330.8 361.26 358.02 356.15 322.56 306.1 303.99 322.23 330.2 343.91 367.07 375.22 375.35 389.81 371.18 387.18 395.43 387.86 392.46 375.11 417.03 408.79 412.68 403.67 414.95 415.35 408.2 424.19 414.03 417.8 418.66 431.35 435.7 438.78 443.38 451.67 440.19 450.23 450.54 448.13 463.55 458.93 467.83 461.93 466.51 481.6 467.19 445.66 450.91 456.5 444.27 458.28 475.49 462.69 472.26 453.55 459.21 470.42 487.39 500.7 514.76 533.4 544.75 562.06 561.88 584.41 581.5 605.37 615.93 636.02 640.43 645.5 654.17 669.12 670.63 639.95 651.99 687.31 705.27 757.02 740.74 786.16 790.82 757.12 801.34 848.28 885.14 954.29 899.47 947.28 914.62 955.4 970.43 980.28 1049.34 1101.75 1111.75 1090.82 1133.84 1120.67 957.28 1017.01 1098.67 1163.63 1129.23 1279.64 1238.33 1286.37 1335.18 1301.84 1372.71 1328.72 1320.41 1282.71 1362.93 1388.91 1469.25 1394.46 1366.42 1498.58 1452.43 1420.6 1454.6 1430.83 1517.68 1436.52 1429.4 1314.95 1320.28 1366.01 1239.94 1160.33 1249.46 1255.82 1224.42 1211.23 1133.58 1040.94 1059.78 1139.45 1148.08 1130.2 1106.73 1147.39 1076.92 1067.14 989.82 911.62 916.07 815.28 885.76 936.31 879.82 855.7 841.15 848.18 916.92 963.59 974.5 990.31 1008.01 995.97 1050.71 1058.2 1111.92 1131.13 1144.94 1113.89 1107.3 1120.68 1140.84 1101.72 1104.24 1114.58 1130.2 1173.78 1211.92 1181.27 1203.6 1180.59 1156.85 1191.5 1191.33 1234.18 1220.33 1228.81 1207.01 1249.48 1248.29 1280.08 1280.66 1302.88 1310.61 1270.05 1270.06 1278.53 1303.8 1335.83 1377.76 1400.63 1418.03 1437.9 1406.8 1420.83 1482.37 1530.63 1504.66 1455.18 1473.96 1527.29 1545.79 1479.63 1467.97 1378.6 1330.45 1326.41 1385.97 1399.62 1276.69 1269.42 1287.83 1164.17 968.67 888.61 902.99 823.09 729.57 793.59 872.74 923.26 920.82 990.22 1019.52 1054.91 1036.18 1098.89
Sample Range:
(leave blank to include all observations)
From:
To:
Number of time lags
36
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)
0
0
1
2
Seasonality
12
12
1
2
3
4
6
12
CI type
White Noise
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 (par6 == 'White Noise') par6 <- 'white' else par6 <- 'ma' par7 <- as.numeric(par7) 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='time lag', ylab='ACF', ci.type=par6, ci=par7, sub=paste('(lambda=',par2,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep='')) 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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Raw Input
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Raw Output
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Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
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