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ARIMA backward selection

*The author of this computation has been verified*
R Software Module: /rwasp_arimabackwardselection.wasp (opens new window with default values)
Title produced by software: ARIMA Backward Selection
Date of computation: Sun, 19 Dec 2010 14:59:42 +0000
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/19/t1292770684pc2qy6hbann2jmy.htm/, Retrieved Sun, 19 Dec 2010 15:58:12 +0100
 
BibTeX entries for LaTeX users:
@Manual{KEY,
    author = {{YOUR NAME}},
    publisher = {Office for Research Development and Education},
    title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/19/t1292770684pc2qy6hbann2jmy.htm/},
    year = {2010},
}
@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Development Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2010},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}
 
Original text written by user:
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
206010 198112 194519 185705 180173 176142 203401 221902 197378 185001 176356 180449 180144 173666 165688 161570 156145 153730 182698 200765 176512 166618 158644 159585 163095 159044 155511 153745 150569 150605 179612 194690 189917 184128 175335 179566 181140 177876 175041 169292 166070 166972 206348 215706 202108 195411 193111 195198 198770 194163 190420 189733 186029 191531 232571 243477 227247 217859 208679 213188 216234 213586 209465 204045 200237 203666 241476 260307 243324 244460 233575 237217 235243 230354 227184 221678 217142 219452 256446 265845 248624 241114 229245 231805 219277 219313 212610 214771 211142 211457 240048 240636 230580 208795 197922 194596 194581 185686 178106 172608 167302 168053 202300 202388 182516 173476 166444 171297 169701 164182 161914 159612 151001 158114 186530 187069 174330 169362 166827 178037 186413 189226 191563 188906 186005 195309 223532 226899 etc...
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time70 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1ma2ma3sar1sar2sma1
Estimates ( 1 )-0.78910.70650.76950.8123-0.4845-0.59330.0323-0.1202-0.5329
(p-val)(1e-04 )(0 )(5e-04 )(0.0016 )(0.0096 )(0.0285 )(0.8871 )(0.3765 )(0.0158 )
Estimates ( 2 )-0.85760.81130.72010.9113-0.6111-0.52240-0.1585-0.4932
(p-val)(0 )(0 )(1e-04 )(4e-04 )(9e-04 )(0.0282 )(NA )(0.1308 )(0 )
Estimates ( 3 )-0.90880.67810.91031.0259-0.4375-0.788700-0.5393
(p-val)(0 )(0 )(0 )(0 )(0.0142 )(0 )(NA )(NA )(0 )
Estimates ( 4 )NANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 5 )NANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 6 )NANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 14 )NANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 15 )NANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 16 )NANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 17 )NANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )


Estimated ARIMA Residuals
Value
-769.911091064411
1171.29374933207
-3576.19909367338
3609.02574272962
1095.79925841030
493.570996581135
1699.03808604918
-836.851790569599
-273.33274927449
2168.44641653895
126.280531276651
-2912.49908022849
2528.279326198
3062.62746113468
1838.54408855953
3170.78275493723
1031.28777198572
1693.88505468331
-534.931294103998
-3998.67897183693
16976.4897466883
4820.76156299178
-4679.06008524019
597.739006023639
-2477.86336887463
243.500170055299
538.858643878335
-3200.23746695186
324.380492584170
1527.48820190478
10152.01213867
-8059.36170074455
-2418.70020158621
1731.69804803241
5707.52053038529
-2457.74088247615
1308.43067614726
-1090.40510831372
239.326738599514
3112.88202848046
735.340966954667
3676.49659050106
7161.95345823981
-5271.52366899354
-615.490931163876
-2953.67362051989
-3959.28228467967
960.006524980228
1416.51784934988
634.824635154175
720.397470155339
-4470.20401491884
712.17396667502
632.714436638679
2421.35179425981
5025.05128520032
-1844.60128865064
7934.36477809165
-2477.42538503866
-2659.07717306397
-4568.25217166251
-2153.24058135901
1086.69403201461
-348.12195537322
-1194.07385286245
975.591846310268
-133.959575019721
-5399.0547322856
-2095.61029664976
-2266.49549333173
-3160.81015683610
1344.92759353098
-11955.0464889042
6424.5906825952
-765.813368053706
7754.3401036934
1287.97876816604
-1575.27353258201
-9483.7660556782
-8373.24725033994
6676.87062136193
-10402.8203505712
-1759.94290062492
-1095.85530323959
5697.04768411452
-3295.26426878776
-2174.62933880444
-1673.19503942257
51.227339272433
894.207408879431
3271.20830644572
-6741.80637755126
-4763.83829373788
5149.95899638157
6359.49322347478
4069.35367233416
861.463305490183
-1076.12541623963
4778.74718653818
660.011275435734
-3644.177055773
4021.36674834023
-5651.22701044024
-6165.26778707089
6483.38099027895
3857.60047521209
6052.5227072953
6726.9391985371
10097.3691486225
4513.37356055597
2970.10060073751
-4046.51950522375
636.105225891238
2404.07792977331
-4585.66701848662
-2297.29019666499
231.391439225268
-214.577648690509
3258.0934402565
8146.9294080479
-10154.0931655095
-2913.35302191135
-4751.01639953772
-1109.69856965135
-1853.91213961220
-2165.27796141261
294.375444732118
-938.131242252736
-3698.26252171547
2449.84996466576
-2776.86349372163
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/19/t1292770684pc2qy6hbann2jmy/1g6he1292770711.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/19/t1292770684pc2qy6hbann2jmy/1g6he1292770711.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/19/t1292770684pc2qy6hbann2jmy/2g6he1292770711.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/19/t1292770684pc2qy6hbann2jmy/2g6he1292770711.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/19/t1292770684pc2qy6hbann2jmy/3g6he1292770711.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/19/t1292770684pc2qy6hbann2jmy/3g6he1292770711.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/19/t1292770684pc2qy6hbann2jmy/4qxgh1292770711.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/19/t1292770684pc2qy6hbann2jmy/4qxgh1292770711.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/19/t1292770684pc2qy6hbann2jmy/5qxgh1292770711.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/19/t1292770684pc2qy6hbann2jmy/5qxgh1292770711.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/19/t1292770684pc2qy6hbann2jmy/6qxgh1292770711.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/19/t1292770684pc2qy6hbann2jmy/6qxgh1292770711.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/19/t1292770684pc2qy6hbann2jmy/7qxgh1292770711.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/19/t1292770684pc2qy6hbann2jmy/7qxgh1292770711.ps (open in new window)


 
Parameters (Session):
par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
 
Parameters (R input):
par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
 
R code (references can be found in the software module):
library(lattice)
if (par1 == 'TRUE') par1 <- TRUE
if (par1 == 'FALSE') par1 <- FALSE
par2 <- as.numeric(par2) #Box-Cox lambda transformation parameter
par3 <- as.numeric(par3) #degree of non-seasonal differencing
par4 <- as.numeric(par4) #degree of seasonal differencing
par5 <- as.numeric(par5) #seasonal period
par6 <- as.numeric(par6) #degree (p) of the non-seasonal AR(p) polynomial
par7 <- 3
par8 <- as.numeric(par8) #degree (P) of the seasonal AR(P) polynomial
par9 <- as.numeric(par9) #degree (Q) of the seasonal MA(Q) polynomial
armaGR <- function(arima.out, names, n){
try1 <- arima.out$coef
try2 <- sqrt(diag(arima.out$var.coef))
try.data.frame <- data.frame(matrix(NA,ncol=4,nrow=length(names)))
dimnames(try.data.frame) <- list(names,c('coef','std','tstat','pv'))
try.data.frame[,1] <- try1
for(i in 1:length(try2)) try.data.frame[which(rownames(try.data.frame)==names(try2)[i]),2] <- try2[i]
try.data.frame[,3] <- try.data.frame[,1] / try.data.frame[,2]
try.data.frame[,4] <- round((1-pt(abs(try.data.frame[,3]),df=n-(length(try2)+1)))*2,5)
vector <- rep(NA,length(names))
vector[is.na(try.data.frame[,4])] <- 0
maxi <- which.max(try.data.frame[,4])
continue <- max(try.data.frame[,4],na.rm=TRUE) > .05
vector[maxi] <- 0
list(summary=try.data.frame,next.vector=vector,continue=continue)
}
arimaSelect <- function(series, order=c(13,0,0), seasonal=list(order=c(2,0,0),period=12), include.mean=F){
nrc <- order[1]+order[3]+seasonal$order[1]+seasonal$order[3]
coeff <- matrix(NA, nrow=nrc*2, ncol=nrc)
pval <- matrix(NA, nrow=nrc*2, ncol=nrc)
mylist <- rep(list(NULL), nrc)
names <- NULL
if(order[1] > 0) names <- paste('ar',1:order[1],sep='')
if(order[3] > 0) names <- c( names , paste('ma',1:order[3],sep='') )
if(seasonal$order[1] > 0) names <- c(names, paste('sar',1:seasonal$order[1],sep=''))
if(seasonal$order[3] > 0) names <- c(names, paste('sma',1:seasonal$order[3],sep=''))
arima.out <- arima(series, order=order, seasonal=seasonal, include.mean=include.mean, method='ML')
mylist[[1]] <- arima.out
last.arma <- armaGR(arima.out, names, length(series))
mystop <- FALSE
i <- 1
coeff[i,] <- last.arma[[1]][,1]
pval [i,] <- last.arma[[1]][,4]
i <- 2
aic <- arima.out$aic
while(!mystop){
mylist[[i]] <- arima.out
arima.out <- arima(series, order=order, seasonal=seasonal, include.mean=include.mean, method='ML', fixed=last.arma$next.vector)
aic <- c(aic, arima.out$aic)
last.arma <- armaGR(arima.out, names, length(series))
mystop <- !last.arma$continue
coeff[i,] <- last.arma[[1]][,1]
pval [i,] <- last.arma[[1]][,4]
i <- i+1
}
list(coeff, pval, mylist, aic=aic)
}
arimaSelectplot <- function(arimaSelect.out,noms,choix){
noms <- names(arimaSelect.out[[3]][[1]]$coef)
coeff <- arimaSelect.out[[1]]
k <- min(which(is.na(coeff[,1])))-1
coeff <- coeff[1:k,]
pval <- arimaSelect.out[[2]][1:k,]
aic <- arimaSelect.out$aic[1:k]
coeff[coeff==0] <- NA
n <- ncol(coeff)
if(missing(choix)) choix <- k
layout(matrix(c(1,1,1,2,
3,3,3,2,
3,3,3,4,
5,6,7,7),nr=4),
widths=c(10,35,45,15),
heights=c(30,30,15,15))
couleurs <- rainbow(75)[1:50]#(50)
ticks <- pretty(coeff)
par(mar=c(1,1,3,1))
plot(aic,k:1-.5,type='o',pch=21,bg='blue',cex=2,axes=F,lty=2,xpd=NA)
points(aic[choix],k-choix+.5,pch=21,cex=4,bg=2,xpd=NA)
title('aic',line=2)
par(mar=c(3,0,0,0))
plot(0,axes=F,xlab='',ylab='',xlim=range(ticks),ylim=c(.1,1))
rect(xleft = min(ticks) + (0:49)/50*(max(ticks)-min(ticks)),
xright = min(ticks) + (1:50)/50*(max(ticks)-min(ticks)),
ytop = rep(1,50),
ybottom= rep(0,50),col=couleurs,border=NA)
axis(1,ticks)
rect(xleft=min(ticks),xright=max(ticks),ytop=1,ybottom=0)
text(mean(coeff,na.rm=T),.5,'coefficients',cex=2,font=2)
par(mar=c(1,1,3,1))
image(1:n,1:k,t(coeff[k:1,]),axes=F,col=couleurs,zlim=range(ticks))
for(i in 1:n) for(j in 1:k) if(!is.na(coeff[j,i])) {
if(pval[j,i]<.01) symb = 'green'
else if( (pval[j,i]<.05) & (pval[j,i]>=.01)) symb = 'orange'
else if( (pval[j,i]<.1) & (pval[j,i]>=.05)) symb = 'red'
else symb = 'black'
polygon(c(i+.5 ,i+.2 ,i+.5 ,i+.5),
c(k-j+0.5,k-j+0.5,k-j+0.8,k-j+0.5),
col=symb)
if(j==choix) {
rect(xleft=i-.5,
xright=i+.5,
ybottom=k-j+1.5,
ytop=k-j+.5,
lwd=4)
text(i,
k-j+1,
round(coeff[j,i],2),
cex=1.2,
font=2)
}
else{
rect(xleft=i-.5,xright=i+.5,ybottom=k-j+1.5,ytop=k-j+.5)
text(i,k-j+1,round(coeff[j,i],2),cex=1.2,font=1)
}
}
axis(3,1:n,noms)
par(mar=c(0.5,0,0,0.5))
plot(0,axes=F,xlab='',ylab='',type='n',xlim=c(0,8),ylim=c(-.2,.8))
cols <- c('green','orange','red','black')
niv <- c('0','0.01','0.05','0.1')
for(i in 0:3){
polygon(c(1+2*i ,1+2*i ,1+2*i-.5 ,1+2*i),
c(.4 ,.7 , .4 , .4),
col=cols[i+1])
text(2*i,0.5,niv[i+1],cex=1.5)
}
text(8,.5,1,cex=1.5)
text(4,0,'p-value',cex=2)
box()
residus <- arimaSelect.out[[3]][[choix]]$res
par(mar=c(1,2,4,1))
acf(residus,main='')
title('acf',line=.5)
par(mar=c(1,2,4,1))
pacf(residus,main='')
title('pacf',line=.5)
par(mar=c(2,2,4,1))
qqnorm(residus,main='')
title('qq-norm',line=.5)
qqline(residus)
residus
}
if (par2 == 0) x <- log(x)
if (par2 != 0) x <- x^par2
(selection <- arimaSelect(x, order=c(par6,par3,par7), seasonal=list(order=c(par8,par4,par9), period=par5)))
bitmap(file='test1.png')
resid <- arimaSelectplot(selection)
dev.off()
resid
bitmap(file='test2.png')
acf(resid,length(resid)/2, main='Residual Autocorrelation Function')
dev.off()
bitmap(file='test3.png')
pacf(resid,length(resid)/2, main='Residual Partial Autocorrelation Function')
dev.off()
bitmap(file='test4.png')
cpgram(resid, main='Residual Cumulative Periodogram')
dev.off()
bitmap(file='test5.png')
hist(resid, main='Residual Histogram', xlab='values of Residuals')
dev.off()
bitmap(file='test6.png')
densityplot(~resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test7.png')
qqnorm(resid, main='Residual Normal Q-Q Plot')
qqline(resid)
dev.off()
ncols <- length(selection[[1]][1,])
nrows <- length(selection[[2]][,1])-1
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'ARIMA Parameter Estimation and Backward Selection', ncols+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Iteration', header=TRUE)
for (i in 1:ncols) {
a<-table.element(a,names(selection[[3]][[1]]$coef)[i],header=TRUE)
}
a<-table.row.end(a)
for (j in 1:nrows) {
a<-table.row.start(a)
mydum <- 'Estimates ('
mydum <- paste(mydum,j)
mydum <- paste(mydum,')')
a<-table.element(a,mydum, header=TRUE)
for (i in 1:ncols) {
a<-table.element(a,round(selection[[1]][j,i],4))
}
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'(p-val)', header=TRUE)
for (i in 1:ncols) {
mydum <- '('
mydum <- paste(mydum,round(selection[[2]][j,i],4),sep='')
mydum <- paste(mydum,')')
a<-table.element(a,mydum)
}
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,'Estimated ARIMA Residuals', 1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Value', 1,TRUE)
a<-table.row.end(a)
for (i in (par4*par5+par3):length(resid)) {
a<-table.row.start(a)
a<-table.element(a,resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')
 





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FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.


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