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Data:
6.23997569975399e-11 6.73997108398115e-11 7.60996748213444e-11 6.73997049807968e-11 7.44996810521796e-11 7.25996854148587e-11 6.04997333480944e-11 6.60997167149459e-11 7.64996738196023e-11 7.67996682035913e-11 7.69996672964398e-11 7.09996924154202e-11 3.40914229064837 0.518198671957056 1.15704118866789 0.808231976707474 0.360198541958294 2.07674531563883 0.690474527672956 0.746666881988732 -0.799086112313255 1.21532788450243 -1.17208235643699 -0.241790228695119 -6.94785802882734 -1.69573013748214 -2.75059694282488 1.21672516745428 -2.70974735796694 -4.30913045821877 0.783514539465725 -3.14687348834951 3.42384941336352 -1.7837941973149 1.96096911705149 2.0652156249323 -0.404933638861316 1.17859561238876 0.180192387338848 -2.34336854579514 0.0584231240880998 0.8387743468686 -1.62573306136452 0.329962475254994 1.88951829938178 -1.84815908911142 0.903036850931911 4.13144956887879 -7.28280048552732 3.65094057322589 3.5560528927436 -1.44263653153796 3.48671018421141 4.01492616391344 -2.13436913371492 6.68834896654783 0.335969397291516 1.15396744118831 7.84706197259469 0.114142950789169 4.8123943467024 0.0243638642555982 -0.612564794406581 0.168920457267928 3.7231482061716 -2.00154047927298 -4.16046560722731 1.47544806986816 -2.37689939657661 -2.15621968442407 2.00225432465706 -4.5491331335966 2.48722667633258 1.36494374589514 4.0569952203111 -6.50373850960401 10.5891739486145 -3.76383061813802 3.45559944666583 1.41984578978046 -1.73601810073833 7.89651201065316 -1.16703293016614 -4.55872835041259 6.10390029860255 -1.92312070159928 -0.471483429817856 3.08479743747335 -0.039041506100148 0.687461250984656 5.31779538711579 0.824086382103777 -2.38687018501734 6.26153013568748 -3.55465658901374 -2.75133272461492 5.2303030045181 3.23265728102641 -5.18916193105667 10.4020501293219 -4.98300881821531 0.871751878705342 4.40001582793177 -6.3353841953649 6.65105232770666 -6.8612199919762 -9.08028740992934 -1.72566078486629 -15.7051865910819 -9.50107301829425 -8.24829447977537 -14.4192038213904 -6.92616564234788 -11.9971144391322 -7.8956543832591 -5.2179362840146 -11.7298203647335 -9.0298622082618 -2.47109529403267 -6.70059499974186 2.10200793573757 2.22084564608995 4.6165870795321 2.10626399773995 -1.89166078216445 8.05599457520003 -1.74033461972163 3.08017690163068 2.03582397788642 -0.693707654837239 4.28575492302477 2.57773233474805 1.12867145107283 -0.791024685688342 7.97311035413928 -4.84003314219746 9.65539036308003 -11.9564819115271 -0.317449295326077 2.72048248937109 -3.41349545663555 -4.39899994870824 -2.32786529371476 -3.91893362792709 3.71446111790681 -0.482872830428383 -3.75418821843784 -1.87183071339254 -8.13307205050054 3.73750402087053 0.405833009194503 -4.66367127753832 -6.07914224109753 3.35366565170573 -4.56162655645187 -5.74074492105851 0.845241125483336 -9.54437899624682 -6.76580417317322 5.67322209836415 -4.54180212424524 -1.99607679233704 3.30614737952226 -8.28552172121058 -0.540857835814741 2.21963004929071 -3.23948389997639 2.39271799991116 -0.800303431604312 0.907944501031133 -8.03022659823752 2.69270042756778 -7.11910232250653 1.77778786484458 -5.46772824122644 -5.6591946864892 3.59663181232567 -4.02002737686667 -4.82737499657813 0.0317226327090455 -2.25058514457937 -6.69550136132503 1.26260788893818 -2.04373824406672 -7.10437726178583 1.93940934073741 -5.36709910929882 -0.21854697486332 -2.05464353612579 -1.49429894836024 2.00522190563415 -0.339273616598684 0.47697625560983 5.49273258050075 -1.45504625647718 3.06489519968946 -0.283858356926263 3.81709065611228 -3.8704397776027 8.0869185559959 -3.80568525834799 -3.11026209527028 0.828261864097397 8.02015617720328 -2.00702698351144 1.15672369384101 4.70652084952233 -8.7352651925813 8.49655765342151 -1.92470260739202 2.36953054610563 3.57463216561004
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)
Do not include Seasonal Dummies
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)
No Linear Trend
0
1
2
Degree of seasonal differencing (D)
0
1
2
Seasonality
12
1
2
3
4
6
12
CI type
12
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')
Compute
Summary of computational transaction
Raw Input
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Raw Output
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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