Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data:
52.61 65.04 67.54 63.58 57.35 54.93 54.30 58.89 65.95 82.65 100.08 100.68 97.53 92.29 85.08 91.61 93.61 90.40 99.31 107.71 106.18 98.80 99.58 98.85 92.69 91.82 92.63 98.41 94.56 85.78 84.59 83.49 84.68 80.12 84.37 85.94 87.07 84.52 83.13 75.95 70.12 78.10 83.06 87.92 90.21 89.95 97.08 102.08 100.64 97.73 97.61 100.32 102.04 107.80 111.51 110.18 110.08 117.40 119.82 118.79 113.18 122.76 120.43 129.16 132.48 135.68 141.49 122.40 137.06 144.84 154.64 148.04 152.76 172.00 169.03 179.68 190.38 233.23 231.45 244.87 299.12 385.01 381.48 321.56 317.27 323.09 392.72 372.37 386.52 412.83 404.91 406.73 392.41 363.31 357.95 375.10 369.74 386.14 353.40 346.87 362.53 349.87 347.03 332.94 327.48 327.92 308.91 285.71 318.81 284.76 301.04 315.16 388.34 383.37 416.77 423.24 429.90 486.07 394.41 410.93 430.88 447.29 431.65 456.53 452.93 440.90 416.46 451.49 432.00 436.19 428.55 421.40 425.18 437.24 431.92 412.65 419.37 436.40 421.37 423.66 402.45 402.82 400.46 425.73 417.93 403.43 404.96 393.64 399.98 375.93 366.57 353.90 347.51 364.10 328.64 348.01 329.63 350.96 336.16 332.15 349.46 383.64 369.82 345.50 337.80 334.76 338.02 346.74 371.84 375.90 373.31 391.91 374.28 384.69 372.16 371.97 351.76 352.89 330.48 347.70 345.58 360.76 364.40 374.62 369.07 341.80 337.87 336.58 332.66 335.74 321.64 329.38 321.84 324.56 330.90 310.91 318.07 312.36 315.19 332.89 310.67 321.26 316.15 283.87 280.65 280.21 265.93 267.80 278.03 291.86 262.61 264.80 265.67 251.05 256.11 279.75 282.52 288.89 308.46 292.89 280.79 273.61 276.67 277.92 250.28 264.70 268.95 261.69 257.99 251.28 243.14 246.81 224.50 241.25 254.97 261.39 266.67 264.28 270.45 274.97 281.13 300.65 321.12 354.79 318.97 298.71 318.85 327.89 348.19 335.18 332.98 331.04 317.52 325.31 317.59 313.37 313.00 314.77 298.37 311.10 308.79 297.30 293.58 291.35 291.51 289.94 287.07 280.74 294.95 288.98 285.63 294.55 290.67 314.78 306.50 304.48 308.65 307.01 298.59 293.51 294.90 296.14 294.25 291.75 290.49 288.68 310.07 297.45 300.81 301.56 296.89 305.23 298.45 298.75 273.02 266.62 266.06 284.48 275.71 284.19 284.81 267.29 272.95 262.35 246.34 251.03 247.54 254.80 245.08 251.30 261.48 258.85 270.89 257.55 253.08 238.81 241.22 280.75 284.56 289.35 289.56 289.55 305.00 289.22 301.82 293.56 300.59 298.67 311.55 310.08 312.06 309.13 292.31 284.41 290.02 291.52 296.81 315.60 319.63 303.89 300.53 321.84 309.48 307.68 310.53 327.91 343.18 345.48 342.03 349.57 322.50 310.74 318.96 327.53 320.00 320.72 330.86 342.34 322.37 306.86 301.75 307.27 301.30 315.18 342.11 333.18 332.26 332.32 330.00 321.78 318.59 344.78 324.09 322.03 325.32 325.10 335.10 334.66 334.54 341.15 320.47 323.85 328.06 328.93 337.50 335.65 361.05 353.19 352.28 392.53 393.03 420.42 434.91 468.38 466.35 480.93 511.25 508.39 479.80 495.63 487.09 473.06 473.03 487.87 479.28 500.60 502.82 497.13 496.06 489.80 481.66 486.17 492.94 522.45 545.71 533.77 570.26 623.56 639.94 589.13 559.45 569.96 590.43 588.37 565.80 629.69 576.28 641.89 625.70 717.52 749.58 690.29 666.55 689.18 666.24 662.32 665.83 681.23 704.87 783.13 757.97 775.93 812.08 824.40 886.89 984.07 1015.59 897.30 980.37 957.37 968.96 1062.80 1047.67 967.91 1021.58 1014.02 1034.98 1068.80 1038.38 1133.26 1259.55 1207.42 1234.59 1297.03
Sample Range:
(leave blank to include all observations)
From:
To:
Number of time lags
Default
Default
5
6
7
8
9
10
11
12
24
36
48
60
Box-Cox transformation parameter (Lambda)
1
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
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 (par8 != '') par8 <- as.numeric(par8) ox <- x if (par8 == '') { if (par2 == 0) { x <- log(x) } else { x <- (x ^ par2 - 1) / par2 } } else { x <- log(x,base=par8) } if (par3 > 0) x <- diff(x,lag=1,difference=par3) if (par4 > 0) x <- diff(x,lag=par5,difference=par4) bitmap(file='picts.png') op <- par(mfrow=c(2,1)) plot(ox,type='l',main='Original Time Series',xlab='time',ylab='value') if (par8=='') { mytitle <- paste('Working Time Series (lambda=',par2,', d=',par3,', D=',par4,')',sep='') mysub <- paste('(lambda=',par2,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep='') } else { mytitle <- paste('Working Time Series (base=',par8,', d=',par3,', D=',par4,')',sep='') mysub <- paste('(base=',par8,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep='') } plot(x,type='l', main=mytitle,xlab='time',ylab='value') par(op) dev.off() bitmap(file='pic1.png') racf <- acf(x, par1, main='Autocorrelation', xlab='time lag', ylab='ACF', ci.type=par6, ci=par7, sub=mysub) dev.off() bitmap(file='pic2.png') rpacf <- pacf(x,par1,main='Partial Autocorrelation',xlab='lags',ylab='PACF',sub=mysub) 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')
Compute
Summary of computational transaction
Raw Input
view raw input (R code)
Raw Output
view raw output of R engine
Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
Click here to blog (archive) this computation