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Data:
2.80030819572237 0.546110929167911 -1.08882457056234 1.85116612407754 1.81431171737364 1.10336738027983 -1.1245171435247 1.71646640041695 0.793858512491152 -1.07059339259452 1.37785351123131 -0.79301277417615 -0.213022653692917 0.325551388183876 -3.07152677312816 -1.6593526905903 -0.977898156347797 2.77746716194583 0.659684775709612 -0.399824712085136 -1.94483744059989 2.63902152884315 0.830351272801263 0.239452118133438 -1.98023780133566 -3.14893729642629 1.52344843344618 1.52324864513037 3.07560476768955 1.68333908953913 -0.0411419750371334 -1.42576307236563 0.603469563697563 -2.02817409689518 2.36128930601495 -4.84900520812879 -0.296289875821015 -0.273406341522266 -2.41797545929271 -0.735276518673968 -0.758208236878704 0.719357808400705 3.82579134038112 0.751147004565213 -0.686160672385733 2.7839041091014 -0.353356298124068 -1.19565368260915 1.51622971016679 0.687899021959271 -1.03746150319703 0.232258589016444 0.954677970598071 0.253726601750312 0.0906111040418593 -0.337464765144114 -0.146645835344418 -2.14580429676713 -2.64273856198557 -1.69885035349378 0.0886900288640063 2.94885012445766 2.74086814209461 -2.78292060200689 2.11354456172898 1.5019821601947 0.928451976610637 0.251386956762454 1.92686717410181 -0.637152825120997 -3.00404371957499 1.53170453971748 -0.46034424964922 -0.435142320623821 0.241189107006538 -0.439858435973453 -1.66304983417195 1.54593083942837 0.0543382827538832 -2.76444539571847 -1.5519700760652 1.99785210877644 1.02671169946072 0.965014171087942 0.833645548583962 -1.78443597034853 -1.35462195476152 0.0905629201358726 1.2672859043733 1.3367497955578 1.00922794934105 2.97970594008853 -0.0198870686489594 1.37558503989304 1.62130116503375 -0.380035665253275 0.140633397218948 1.088271459054 -4.14041609113554 -1.36191185169048 -3.501894103396 1.82567198282555 1.00217218900356 -1.70508751233354 -1.54985320913675 1.52749755170791 -1.11909042570084 0.782613205637535 0.394327211581645 1.03045019427192 -1.98850357932067 -2.3911175541089 -2.49477174792859 1.43479131886814 -0.23693199243609 1.54252687780381 0.753916630559178 -1.12999945988543 0.434385612031916 0.497954292194177 -0.415657064566058 1.25212054898028 0.538408325374887 0.363650201264021 -0.525700021635337 -1.63043333745253 0.651092396769283 0.605424752110409 1.27467788471578 -3.66701110704255 -1.09455091524368 -2.01546316552593 -0.129017313491364 1.80505691986507 -2.93931661204688 0.266590824347748 1.08312554226064 -0.0657619162545335 1.90954638692403 -1.52044597892013 0.720915677264753 -1.30223881899939 1.12662982290344 -0.651780253148442 0.873322331496494 -0.545999882625613 -0.386205647008669 2.73997287136118 -0.246626845056549 2.49376474567805 -2.78811070614103 -0.470542681359586 1.71855478201569 1.18881581030952 2.56216432022585 0.443671331515407 -1.46466491183079 -2.05654394411988 1.31101180554756 -1.38802306664201 -1.42282760393946 -0.770545943306666 0.204099941303661 -3.73653059676392 2.41609709604484 0.459211471769727 -1.95214272484412 -0.467671553906553 -2.54700443680883 0.530260345025432 2.59956805328044 -0.048523881273836 -1.31831688661928 1.0921477226447 1.49992331623734 -1.21692016754482 -0.350713444316563 -0.709451653003092 0.660733517233603
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
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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