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Data:
87.28 255 87.28 280.2 87.09 299.9 86.92 339.2 87.59 374.2 90.72 393.5 90.69 389.2 90.3 381.7 89.55 375.2 88.94 369 88.41 357.4 87.82 352.1 87.07 346.5 86.82 342.9 86.4 340.3 86.02 328.3 85.66 322.9 85.32 314.3 85 308.9 84.67 294 83.94 285.6 82.83 281.2 81.95 280.3 81.19 278.8 80.48 274.5 78.86 270.4 69.47 263.4 68.77 259.9 70.06 258 73.95 262.7 75.8 284.7 77.79 311.3 81.57 322.1 83.07 327 84.34 331.3 85.1 333.3 85.25 321.4 84.26 327 83.63 320 86.44 314.7 85.3 316.7 84.1 314.4 83.36 321.3 82.48 318.2 81.58 307.2 80.47 301.3 79.34 287.5 82.13 277.7 81.69 274.4 80.7 258.8 79.88 253.3 79.16 251 78.38 248.4 77.42 249.5 76.47 246.1 75.46 244.5 74.48 243.6 78.27 244 80.7 240.8 79.91 249.8 78.75 248 77.78 259.4 81.14 260.5 81.08 260.8 80.03 261.3 78.91 259.5 78.01 256.6 76.9 257.9 75.97 256.5 81.93 254.2 80.27 253.3 78.67 253.8 77.42 255.5 76.16 257.1 74.7 257.3 76.39 253.2 76.04 252.8 74.65 252 73.29 250.7 71.79 252.2 74.39 250 74.91 251 74.54 253.4 73.08 251.2 72.75 255.6 71.32 261.1 70.38 258.9 70.35 259.9 70.01 261.2 69.36 264.7 67.77 267.1 69.26 266.4 69.8 267.7 68.38 268.6 67.62 267.5 68.39 268.5 66.95 268.5 65.21 270.5 66.64 270.9 63.45 270.1 60.66 269.3 62.34 269.8 60.32 270.1 58.64 264.9 60.46 263.7 58.59 264.8 61.87 263.7 61.85 255.9 67.44 276.2 77.06 360.1 91.74 380.5 93.15 373.7 94.15 369.8 93.11 366.6 91.51 359.3 89.96 345.8 88.16 326.2 86.98 324.5 88.03 328.1 86.24 327.5 84.65 324.4 83.23 316.5 81.7 310.9 80.25 301.5 78.8 291.7 77.51 290.4 76.2 287.4 75.04 277.7 74 281.6 75.49 288 77.14 276 76.15 272.9 76.27 283 78.19 283.3 76.49 276.8 77.31 284.5 76.65 282.7 74.99 281.2 73.51 287.4 72.07 283.1 70.59 284 71.96 285.5 76.29 289.2 74.86 292.5 74.93 296.4 71.9 305.2 71.01 303.9 77.47 311.5 75.78 316.3 76.6 316.7 76.07 322.5 74.57 317.1 73.02 309.8 72.65 303.8 73.16 290.3 71.53 293.7 69.78 291.7 67.98 296.5 69.96 289.1 72.16 288.5 70.47 293.8 68.86 297.7 67.37 305.4 65.87 302.7 72.16 302.5 71.34 303 69.93 294.5 68.44 294.1 67.16 294.5 66.01 297.1 67.25 289.4 70.91 292.4 69.75 287.9 68.59 286.6 67.48 280.5 66.31 272.4 64.81 269.2 66.58 270.6 65.97 267.3 64.7 262.5 64.7 266.8 60.94 268.8 59.08 263.1 58.42 261.2 57.77 266 57.11 262.5 53.31 265.2 49.96 261.3 49.4 253.7 48.84 249.2 48.3 239.1 47.74 236.4 47.24 235.2 46.76 245.2 46.29 246.2 48.9 247.7 49.23 251.4 48.53 253.3 48.03 254.8 54.34 250 53.79 249.3 53.24 241.5 52.96 243.3 52.17 248 51.7 253 58.55 252.9 78.2 251.5 77.03 251.6 76.19 253.5 77.15 259.8 75.87 334.1 95.47 448 109.67 445.8 112.28 445 112.01 448.2 107.93 438.2 105.96 439.8 105.06 423.4 102.98 410.8 102.2 408.4 105.23 406.7 101.85 405.9 99.89 402.7 96.23 405.1 94.76 399.6 91.51 386.5 91.63 381.4 91.54 375.2 85.23 357.7 87.83 359 87.38 355 84.44 352.7 85.19 344.4 84.03 343.8 86.73 338 102.52 339 104.45 333.3 106.98 334.4 107.02 328.3 99.26 330.7 94.45 330 113.44 331.6 157.33 351.2 147.38 389.4 171.89 410.9 171.95 442.8 132.71 462.8 126.02 466.9 121.18 461.7 115.45 439.2 110.48 430.3 117.85 416.1 117.63 402.5 124.65 397.3 109.59 403.3 111.27 395.9 99.78 387.8 98.21 378.6 99.2 377.1 97.97 370.4 89.55 362 87.91 350.3 93.34 348.2 94.42 344.6 93.2 343.5 90.29 342.8 91.46 347.6 89.98 346.6 88.35 349.5 88.41 342.1 82.44 342 79.89 342.8 75.69 339.3 75.66 348.2 84.5 333.7 96.73 334.7 87.48 354 82.39 367.7 83.48 363.3 79.31 358.4 78.16 353.1 72.77 343.1 72.45 344.6 68.46 344.4 67.62 333.9 68.76 331.7 70.07 324.3 68.55 321.2 65.3 322.4 58.96 321.7 59.17 320.5 62.37 312.8 66.28 309.7 55.62 315.6 55.23 309.7 55.85 304.6 56.75 302.5 50.89 301.5 53.88 298.8 52.95 291.3 55.08 293.6 53.61 294.6 58.78 285.9 61.85 297.6 55.91 301.1 53.32 293.8 46.41 297.7 44.57 292.9 50 292.1 50 287.2 53.36 288.2 46.23 283.8 50.45 299.9 49.07 292.4 45.85 293.3 48.45 300.8 49.96 293.7 46.53 293.1 50.51 294.4 47.58 292.1 48.05 291.9 46.84 282.5 47.67 277.9 49.16 287.5 55.54 289.2 55.82 285.6 58.22 293.2 56.19 290.8 57.77 283.1 63.19 275 54.76 287.8 55.74 287.8 62.54 287.4 61.39 284 69.6 277.8 79.23 277.6 80 304.9 93.68 294 107.63 300.9 100.18 324 97.3 332.9 90.45 341.6 80.64 333.4 80.58 348.2 75.82 344.7 85.59 344.7 89.35 329.3 89.42 323.5 104.73 323.2 95.32 317.4 89.27 330.1 90.44 329.2 86.97 334.9 79.98 315.8 81.22 315.4 87.35 319.6 83.64 317.3 82.22 313.8 94.4 315.8 102.18 311.3
Sample Range:
(leave blank to include all observations)
From:
To:
Number of time lags
6
Default
5
6
7
8
9
10
11
12
24
36
48
60
Box-Cox transformation parameter (Lambda)
2
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)
Pearson Chi-Squared
0
1
2
Degree of seasonal differencing (D)
0
1
2
Seasonality
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 (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 Output
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Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
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