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Data:
1.72923686058208 0.122433126801145 0.523768788982079 -0.50175942601324 -1.86386120887019 0.0717422351702872 -0.380578531491989 -0.29851611788274 1.94316215667028 -1.24779901378139 1.35257277275093 2.84406850014719 1.12274878943606 -2.32540657982629 -2.89902363942181 0.246874012745119 -0.731094375584061 1.97769407549524 -0.089308186457892 -1.64931358279907 1.78988776616251 -0.655026882699021 -0.95518008982139 -0.501427837065876 -1.47468882156665 2.4255103110643 -0.388732570521789 -1.41970373897772 -0.435743764210552 0.588337253253952 -1.73087780677345 0.416192838768035 -0.135602934239543 1.5607508968729 -0.619488852927662 0.130061173484127 -0.485714783249833 1.19042117073251 0.504940688912012 -0.266725732456376 1.8987616014054 0.9835731579105 0.221023228988728 -0.176860990484583 -0.162198287814433 0.78911590880292 1.41209201053576 0.518161844628975 2.36462043434148 -1.24446105804486 1.09138861156198 0.422175612267996 -0.997989397904933 0.754650745534752 -1.44375392644115 0.568632265545353 1.35413305240232 -1.34288885715882 -0.0968579661829751 0.242767533935212 0.255744783559028 -0.243232899636027 0.461607264600104 0.0650797112167817 2.25999343044582 1.80462197944889 2.32548979115297 0.682758803307819 -0.335554613197298 -0.997929583796738 0.97507128242145 -1.17268516900173 -0.000337278483281606 -1.71460596672184 -1.34872342854985 -1.6521456119588 -1.48335049731373 0.925016701443022 -0.226525557050897 0.774905929042345 -0.339789522342372 0.89311055438168 -0.864578780222597 -1.95217915873499 -0.11317194501752 1.02470860347022 -1.25650654087286 -0.602130638597027 -0.83233134657693 -0.446577198612737 -4.01053303468812 0.288513333839785 -1.00798474916531 -0.572816189634855 -0.297410570159231 1.21578539483534 -0.727486991378146 -0.843943364912431 -1.20889338911855 -0.649656515850742 3.19984732249364 0.572738529480399 -0.0199646221520179 0.462316869083462 -1.28699501625665 -1.80757939159691 -2.15431670426712 0.707840316820782 1.36410372036211 0.314718394752158 -4.1002515835693 1.26992188862053 1.80144339707427 -0.42970220964945 0.529993653035631 1.18764422512472 -0.398233352310507 -0.421884162222733 -2.6898323919027 1.0140628812162 -0.542058945797581 -0.368690243263354 1.31411524584048 -0.428705333764204 -0.663582558495647 1.37204511801761 -1.27310996987184 0.810157913937622 1.5741305304336 -1.025150115941 -0.998930862337702 -0.0536208441845616 1.66337771592794 -0.178008462116163 -1.09164775518912 1.96797905166753 0.634220496887586 1.38764487483822 0.75570654001001 -2.3214750727902 1.62023993830839 0.423224828517651 -1.7051130142733 0.541981863740972 -0.593027838089475 0.213567450337977 -0.696840242545287 0.0777251570183703 -0.725328497765991 2.1315776363419 -0.33645518676905 -0.979182554450992 -0.242246084610725 -1.82980075345691 -1.14814874978528 0.496126806273696 -0.37803015357093 0.564631359789787 0.268965939275992 0.752661368724124 -2.30203173253409 2.82635859577673 -0.607627712639158 3.77405193258893 1.08426693162876 -2.2508985634727 0.290663952981133 -1.1346060583543 1.41583984485642 0.590041695581542 1.22056539827692 0.0970995178245223 -0.906020145960084 -0.597681226735446 0.437155726246844 0.742487563055592 1.22917286536281 -0.95259160353182 -0.75528682870179
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)
0
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 (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')
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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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