Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data:
600.58 602.89 549.64 476.58 469.19 540.02 587.12 574.71 623.85 645.39 615.15 539.13 603.24 544.14 536.36 561.37 570.83 563.66 515.46 509.39 526.94 504.54 465.72 472.78 453.41 456.42 407.65 417.80 400.15 396.14 425.40 465.05 565.15 554.02 527.61 540.11 602.28 607.94 520.70 544.05 564.13 536.55 544.57 576.06 555.20 528.02 526.32 540.82 533.61 514.22 522.10 529.87 527.42 537.63 513.46 513.21 527.32 526.93 540.06 515.31 490.06 507.63 480.66 513.92 499.70 495.28 452.26 469.19 440.63 435.96 417.73 404.87 421.91 384.75 409.63 380.59 404.85 385.26 373.41 395.12 432.87 447.73 401.91 387.92 377.51 374.66 377.40 400.38 429.18 420.64 427.39 427.99 432.69 413.84 390.97 393.18 408.37 382.31 376.13 385.17 398.23 420.28 420.89 413.39 397.34 367.80 373.07 381.80 385.16 362.50 377.22 369.18 376.74 366.94 357.76 365.95 345.59 344.81 358.99 355.67 352.57 360.86 340.21 321.56 319.21 301.28 295.81 315.21 311.70 295.92 293.79 287.73 292.31 283.23 318.46 317.40 314.78 339.93 328.29 317.46 296.54 306.94 300.02 280.23 292.65 296.25 289.08 287.43 277.98 266.25 266.37 247.22 246.10 271.03 274.35 276.29 268.08 276.63 272.45 276.36 298.83 320.92 349.04 322.99 295.04 319.37 324.63 339.90 339.85 332.66 332.07 317.71 325.43 316.45 313.44 310.46 312.21 295.69 307.54 302.60 288.98 281.13 276.01 275.15 277.27 273.59 270.56 286.55 280.07 275.80 285.34 283.19 308.44 301.39 298.99 306.65 305.04 297.07 290.54 291.85 295.36 293.65 291.83 293.33 287.84 313.96 298.87 301.34 300.15 297.56 306.71 299.08 300.19 275.67 267.61 265.49 266.11 273.22 272.63 281.99 269.60 265.28 266.59 257.87 249.87 246.32 251.70 247.93 247.43 256.62 262.89 264.16 259.75 251.82 247.44 242.02 252.07 290.39 283.82 279.99 280.68 304.87 296.86 295.62 304.04 300.60 299.61 303.41 313.88 316.14 311.05 303.86 283.10 284.38 289.38 291.92 311.66 316.46 310.94 302.71 311.31 312.38 310.77 308.94 319.05 339.77 335.61 341.44 343.23 335.31 315.68 317.35 325.56 322.59 318.61 326.54 336.02 333.15 314.95 302.48 307.31 305.50 308.57 322.58 337.09 323.81 333.06 331.90 327.90 319.93 331.51 336.42 319.77 323.20 324.51 328.34 331.88 336.45 337.95 330.75 323.87 325.26 328.73 331.72 332.54 354.25 352.69 356.15 372.50 390.90 404.65 430.04 453.54 464.98 463.31 497.20 528.62 470.91 499.53 493.51 469.97 464.41 487.15 476.45 484.91 509.61 495.19 504.75 493.43 488.58 484.82 488.46 512.32 530.29 549.38 551.45 604.41 625.29 623.56 577.42 572.28 571.69 596.28 560.00 577.93 606.51 597.31 607.58 648.14 737.48 708.73 674.01 679.90 674.93 663.38 665.69 684.21 703.71 755.42 772.43
Sample Range:
(leave blank to include all observations)
From:
To:
Number of time lags
12
Default
5
6
7
8
9
10
11
12
24
36
48
60
Box-Cox transformation parameter (Lambda)
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)
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 (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
1 seconds
R Server
Big Analytics Cloud Computing Center
Click here to blog (archive) this computation