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Data:
0.0623999591436363 0.0673999230710553 0.0760999147466414 0.0673999158700459 0.074499915794244 0.0725999133063456 0.0604999222274966 0.0660999249209322 0.0764999151680504 0.0767999095051533 0.0769999092165195 0.0709999179335117 10.8359819154429 1.26205617642799 3.66239571825825 2.46947788071442 1.10714520406324 6.58335475310772 1.97149556979767 2.334280435698 -2.54332601659813 4.01793958263306 -3.78133437401082 -0.317979846061568 0.0102718897785343 -1.25092729860763 -0.700155963789233 5.94746185533375 -4.21943896535866 0.0339173356005719 4.53904960009886 -3.46259688626591 3.90893355567736 1.31620485490087 -0.828659050187798 3.53943098957265 2.89682859418273 1.28902917402445 0.852377148177291 2.06860038524397 -2.9633056377674 3.6089269689262 1.82308335334898 -1.51103941511939 5.46562585988295 -0.644132413147318 -0.421749518113039 9.41909500288001 -6.05483250613297 6.26316203786592 6.1061754426878 1.98486913639551 1.94474939888551 9.49900339871634 0.1183519039818 8.08575085363917 4.15008198724068 2.8766386151856 9.56361825989926 6.46128841992948 5.51866978266072 2.46641004271275 2.02362383855627 4.39158791305629 3.0846546589005 2.88579769666626 -2.81067389296319 3.8462301336442 1.87916828895322 -0.9943336802728 6.1403409537484 1.28922355434657 4.21553104873813 4.51846951729731 7.67243139808268 -4.46223259853389 13.2024399740054 -0.244031876757103 5.41093176400443 3.8159915787943 2.80610198743267 10.5665637320416 2.67734693845842 1.36522945194721 7.49062940807855 1.74801742338373 4.2703015092933 5.61580033453356 3.20512098763879 5.91149113506666 7.99354266370528 5.00867951139651 2.00724027563501 10.971195617128 1.54079973406887 3.1065134315452 8.34364150707301 7.72232565827425 -1.7480410474781 15.5088148870299 -2.30566904607059 6.22888409274236 6.99346124253593 -2.88325446701002 12.5835936098068 -4.49331125710061 -4.21932556933798 3.47763297430723 -15.1602623934305 -5.34672068341061 -4.470010160039 -13.5293379673141 -1.57510981243814 -9.55988965698972 -5.39297295794674 -1.52499761685971 -8.14052182850979 -5.37127486427748 2.42984671252665 -3.50408497116221 5.117138251551 6.25215906125452 9.19563568594373 4.24938824221677 2.95387448832031 12.6178133571214 1.22875472385876 7.74809965968539 5.98957899323123 4.30017082282983 9.32158519770366 6.03625848178254 4.74482656426815 3.95397680109579 12.4097744963252 -1.19324637568364 15.2879482307578 -9.81885697627214 4.46997486430695 6.29489181794462 1.75997238228665 -1.48658629757794 0.784181263377334 -1.00008124325305 4.24357645474808 2.35213512377959 -1.97504254506926 -1.0653775346474 -5.35622063327614 5.44623239713341 2.78694146260069 -2.7377059415704 -3.59718697363833 5.46930549780257 -2.15237621817309 -4.56770152209916 1.9717584189804 -6.95021543713611 -3.58145342165933 7.91998231221258 -1.71235630304608 0.944188611634672 6.04850757155987 -5.90068291709636 3.21877052425239 4.41132377437188 0.64475864453965 4.98696080764906 0.380582102752953 4.80403142656398 -3.64219595151302 3.96653248577481 -1.93618163475807 2.54959953147041 -3.44411511575284 -1.96036850131122 7.29707303571976 -3.43439634089813 -1.36653579470006 1.60885399217194 -0.581524305989163 -3.75829179017525 5.75966851954242 -2.17148776196537 -3.58329387819147 3.48292030892493 -3.21882884148393 2.83257985033849 -0.549241047179422 0.440097080020692 5.0380454108796 -0.476799846408127 2.44966216318491 7.57640070239071 0.682426725323751 4.99683860901307 2.78306349913964 4.95154425648789 -0.883660293995149 10.1021411015655 -2.58263176041314 -0.790850868174502 3.34369988033605 9.47287447684811 -1.02516817879373 3.28001608825431 6.31270146516164 -7.83284885616155 11.8457995134525 -1.61829145177705 4.94176038091014 3.88374059882783
Sample Range:
(leave blank to include all observations)
From:
To:
Number of time lags
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
par8 <- '' par7 <- '0.99' par6 <- 'White Noise' par5 <- '12' par4 <- '0' par3 <- '0' par2 <- '1' par1 <- 'Default' 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) x <- na.omit(x) 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,'ACF(k)',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,'PACF(k)',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
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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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