Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data:
6.23998845945663e-05 6.7399848660904e-05 7.60998304324026e-05 6.73998428018774e-05 7.44998333915192e-05 7.25998338690263e-05 6.04998570598915e-05 6.60998518777687e-05 7.64998302485913e-05 7.67998252460185e-05 7.69998247478316e-05 7.09998375978707e-05 6.96972919861135 1.0594753243103 2.36553671564381 1.65242079172956 0.736466217230401 4.24578070133915 1.41166999320756 1.52655551749694 -1.63357883987514 2.4846990147631 -2.3961336343665 -0.494245993072444 -4.08433953352584 -1.59988707936486 -1.98701539119398 3.41562852165754 -3.45567986000423 -2.60543919929562 2.52621154440284 -3.38218438124645 3.70557631155031 -0.395094425292799 0.749555752063829 2.69674931154964 -0.0316212938847186 1.04580227630627 0.0368549960672206 -0.259991854378262 -1.78059874471568 1.36824260708871 0.0366356157200839 -1.01720536754601 4.16322218984067 -1.63443634171084 0.625290765294417 7.05052283915173 -8.77817244331685 4.75784225772246 4.38910900863377 -0.138812516359707 2.42142927047071 5.88073685660006 -1.28428763541089 7.05615376351572 3.33785544939206 1.24197573076387 9.76877553458446 4.33646155751931 2.57875110676098 1.81273018788077 0.692603976946662 1.36575287061977 3.89007254199292 0.137760515939454 -4.47601708822027 3.27833859040915 0.526896851498776 -2.36637215355483 5.98557285567742 -0.887651504499398 1.13879697599447 3.51283518105807 6.00855402671302 -6.42266017096702 13.0727665969817 -2.45504350972329 3.05161567000849 3.75163300870277 0.795971661193441 8.61039809331774 3.1738050759665 -1.94177342264319 5.50791153744771 0.34083788893098 2.08013333376821 2.80074416076483 3.87354927445471 1.86425747623542 5.95105128237998 3.7969585313084 -0.242022236482244 8.81978059708492 0.673443542733268 -0.890904130702688 5.92559380408292 5.83664185878208 -3.26132401985848 11.6869776360399 -1.39161589445232 2.18178417419402 5.906179074476 -3.8876042555906 9.42975280882809 -4.60724863521589 -6.0166599816354 -0.42791965785768 -16.3062141162499 -7.57291588623926 -7.42055242114805 -14.2129765537761 -3.92085288581029 -12.1008808104756 -6.80266100528509 -3.67510942275168 -9.74228594370342 -7.94891658835165 -0.259081218012531 -6.43324133833984 0.910740045335072 3.77668329726579 5.41490878091593 1.70012171157033 0.642146325512913 8.00894996030589 -1.09264706929533 4.71197898090644 3.45921425787565 0.0423547502377837 6.74346784053631 2.56306523028196 0.305373278103549 0.977288958686864 9.56427646414573 -5.21152570290375 12.8901849179401 -12.6714328646935 0.390179139637417 4.58231870782921 -1.91365058175955 -4.3378424570296 -0.123318900214041 -4.25748802446382 2.61892530906964 0.899732765270311 -2.41725304555932 -3.18643907421467 -5.31542963250333 2.30477499580293 0.844150216262817 -3.22996990141361 -5.65092443705939 3.14337099440955 -2.88662800021262 -7.03693679650081 -0.034404760393665 -8.82296372315021 -5.90178644450552 4.72499165795161 -2.3646674065666 -3.22501369180204 3.93796515158186 -7.51717071656621 -0.330575634129549 2.25857935490991 -1.7823399770245 1.08896014335903 -1.68671598287488 1.38692018864012 -7.63503984844527 1.89932183658348 -5.16733833082343 0.118681102107675 -5.1296943953283 -5.43457931636764 3.91402515664198 -4.37651179166394 -3.88101359054992 -1.40828931946199 -3.20577310233524 -6.6729118906251 1.38327522212045 -3.0243004228396 -6.07055523004141 0.507119614644546 -5.51904523592491 -0.343157917114292 -2.038566599003 -1.95829988414772 2.7845106817365 -2.0968052917193 -0.497776997429464 5.33922919489525 -1.6860467588996 2.40920341561679 0.404902136942292 2.54569658186192 -4.27557156872314 8.12600596004447 -4.11974673264829 -3.74217845210401 1.57428849837183 6.77662968716753 -3.15836151496727 1.17044811932514 4.40355076970579 -9.78272468840314 9.42133794155187 -3.23488223996661 1.80420544732101 3.7373470810681
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)
Pearson Chi-Squared
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 <- '0.95' 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
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