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Data:
jan/00 41086 feb/00 39690 mrt/00 43129 apr/00 37863 mei/00 35953 jun/00 29133 jul/00 24693 aug/00 22205 sep/00 21725 okt/00 27192 nov/00 21790 dec/00 13253 jan/01 37702 feb/01 30364 mrt/01 32609 apr/01 30212 mei/01 29965 jun/01 28352 jul/01 25814 aug/01 22414 sep/01 20506 okt/01 28806 nov/01 22228 dec/01 13971 jan/02 36845 feb/02 35338 mrt/02 35022 apr/02 34777 mei/02 26887 jun/02 23970 jul/02 22780 aug/02 17351 sep/02 21382 okt/02 24561 nov/02 17409 dec/02 11514 jan/03 31514 feb/03 27071 mrt/03 29462 apr/03 26105 mei/03 22397 jun/03 23843 jul/03 21705 aug/03 18089 sep/03 20764 okt/03 25316 nov/03 17704 dec/03 15548 jan/04 28029 feb/04 29383 mrt/04 36438 apr/04 32034 mei/04 22679 jun/04 24319 jul/04 18004 aug/04 17537 sep/04 20366 okt/04 22782 nov/04 19169 dec/04 13807 jan/05 29743 feb/05 25591 mrt/05 29096 apr/05 26482 mei/05 22405 jun/05 27044 jul/05 17970 aug/05 18730 sep/05 19684 okt/05 19785 nov/05 18479 dec/05 10698
Type of Seasonality
additive
additive
multiplicative
Seasonal Period
12
12
1
2
3
4
5
6
7
8
9
10
11
12
Chart options
R Code
par2 <- as.numeric(par2) x <- ts(x,freq=par2) m <- decompose(x,type=par1) m$figure bitmap(file='test1.png') plot(m) dev.off() mylagmax <- length(x)/2 bitmap(file='test2.png') op <- par(mfrow = c(2,2)) acf(as.numeric(x),lag.max = mylagmax,main='Observed') acf(as.numeric(m$trend),na.action=na.pass,lag.max = mylagmax,main='Trend') acf(as.numeric(m$seasonal),na.action=na.pass,lag.max = mylagmax,main='Seasonal') acf(as.numeric(m$random),na.action=na.pass,lag.max = mylagmax,main='Random') par(op) dev.off() bitmap(file='test3.png') op <- par(mfrow = c(2,2)) spectrum(as.numeric(x),main='Observed') spectrum(as.numeric(m$trend[!is.na(m$trend)]),main='Trend') spectrum(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal') spectrum(as.numeric(m$random[!is.na(m$random)]),main='Random') par(op) dev.off() bitmap(file='test4.png') op <- par(mfrow = c(2,2)) cpgram(as.numeric(x),main='Observed') cpgram(as.numeric(m$trend[!is.na(m$trend)]),main='Trend') cpgram(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal') cpgram(as.numeric(m$random[!is.na(m$random)]),main='Random') par(op) dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Classical Decomposition by Moving Averages',6,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'t',header=TRUE) a<-table.element(a,'Observations',header=TRUE) a<-table.element(a,'Fit',header=TRUE) a<-table.element(a,'Trend',header=TRUE) a<-table.element(a,'Seasonal',header=TRUE) a<-table.element(a,'Random',header=TRUE) a<-table.row.end(a) for (i in 1:length(m$trend)) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,x[i]) if (par1 == 'additive') a<-table.element(a,m$trend[i]+m$seasonal[i]) else a<-table.element(a,m$trend[i]*m$seasonal[i]) a<-table.element(a,m$trend[i]) a<-table.element(a,m$seasonal[i]) a<-table.element(a,m$random[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab')
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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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