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Data:
133448.00 132951.00 132447.00 131404.00 141722.00 141176.00 133448.00 128310.00 128807.00 128807.00 129360.00 130354.00 131901.00 131901.00 130907.00 128310.00 141722.00 143766.00 140679.00 133448.00 136542.00 131901.00 133994.00 134995.00 136038.00 133448.00 133994.00 130354.00 141722.00 145313.00 142226.00 136542.00 142723.00 136038.00 142226.00 141722.00 143269.00 137585.00 143766.00 143269.00 152544.00 150451.00 142226.00 138082.00 143766.00 136038.00 141722.00 142723.00 144816.00 140182.00 142723.00 144270.00 149954.00 145313.00 139132.00 132447.00 138635.00 121625.00 129857.00 134491.00 139132.00 132447.00 132447.00 132447.00 136038.00 130907.00 124173.00 118538.00 122626.00 106666.00 116445.00 122129.00 123172.00 117488.00 117985.00 116445.00 121625.00 117985.00 110810.00 105623.00 114394.00 95347.00 107716.00 113351.00 113351.00 106666.00 100485.00 99988.00 105623.00 100485.00 90713.00 83979.00 91210.00 74207.00 89663.00 97888.00 100485.00 94801.00 87619.00 92757.00 94801.00 93254.00 77791.00 70616.00 75747.00 60291.00 76251.00 81935.00 86569.00 78841.00 71610.00 75747.00 77791.00 73703.00 58247.00 51513.00 57694.00 40691.00 59241.00 70616.00
Type of Seasonality
0
additive
multiplicative
Seasonal Period
no
12
1
2
3
4
5
6
7
8
9
10
11
12
Chart options
R Code
par2 <- '12' par1 <- 'multiplicative' 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$trend)) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,x[i]) if (par1 == 'additive') a<-table.element(a,signif(m$trend[i]+m$seasonal[i],6)) else a<-table.element(a,signif(m$trend[i]*m$seasonal[i],6)) a<-table.element(a,signif(m$trend[i],6)) a<-table.element(a,signif(m$seasonal[i],6)) a<-table.element(a,signif(m$random[i],6)) 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
0 seconds
R Server
Big Analytics Cloud Computing Center
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