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Data:
181896.00 181580.00 181234.00 180598.00 187123.00 186807.00 181896.00 178638.00 178954.00 178954.00 179269.00 179936.00 181580.00 179616.00 181580.00 179936.00 185158.00 187469.00 177656.00 175025.00 177305.00 176989.00 175025.00 175345.00 179269.00 178638.00 179269.00 179269.00 183545.00 184176.00 172398.00 172398.00 176989.00 174709.00 170785.00 172398.00 176327.00 174363.00 174047.00 169803.00 176007.00 177305.00 164545.00 164229.00 170785.00 167176.00 160967.00 163598.00 166509.00 167176.00 165212.00 161287.00 169456.00 169456.00 155078.00 154101.00 158025.00 150838.00 143616.00 145932.00 150838.00 146910.00 144283.00 138710.00 146247.00 146563.00 132190.00 131839.00 134470.00 126301.00 117465.00 121043.00 125950.00 120728.00 120412.00 115154.00 123670.00 125319.00 109266.00 105688.00 107968.00 99132.00 89981.00 92928.00 98470.00 91946.00 92928.00 89004.00 97172.00 98150.00 78524.00 77221.00 80799.00 71333.00 62817.00 65764.00 72950.00 64462.00 63799.00 57244.00 64462.00 66742.00 46448.00 46448.00 49391.00 41542.00 32706.00 37297.00 45466.00 36631.00 40244.00 35333.00 43186.00 45813.00 24853.00 23240.00 26502.00 18649.00 12444.00 15040.00
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$trend)) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,x[i]) if (par1 == 'additive') a<-table.element(a,signif(m$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 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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