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Data:
106474.50 106559.75 106636.75 106722.00 106804.50 106889.75 106972.25 107057.50 107142.75 107225.25 107310.50 107393.00 107478.25 107563.50 107640.50 107725.75 107808.25 107893.50 107976.00 108061.25 108146.50 108229.00 108314.25 108396.75 108482.00 108567.25 108647.00 108732.25 108814.75 108900.00 108982.50 109067.75 109153.00 109235.50 109320.75 109403.25 109488.50 109573.75 109650.75 109736.00 109818.50 109903.75 109986.25 110071.50 110156.75 110239.25 110324.50 110407.00 110492.25 110577.50 110654.50 110739.75 110822.25 110907.50 110990.00 111075.25 111160.50 111243.00 111328.25 111410.75 111496.00 111581.25 111658.25 111743.50 111826.00 111911.25 111993.75 112079.00 112164.25 112246.75 112332.00 112414.50 112499.75 112585.00 112664.75 112750.00 112832.50 112917.75 113000.25 113085.50 113170.75 113253.25 113338.50 113421.00 113506.25 113591.50 113668.50 113753.75 113836.25 113921.50 114004.00 114089.25 114174.50 114257.00 114342.25 114424.75 114510.00 114595.25 114672.25 114757.50 114840.00 114925.25 115007.75 115093.00 115178.25 115260.75 115346.00 115428.50 115513.75 115599.00 115676.00 115761.25 115843.75 115929.00 116011.50 116096.75 116182.00 116264.50 116349.75 116432.25
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 Output
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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