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Data:
29054.50 28543.50 28032.00 27009.50 37356.00 36844.50 29054.50 23881.50 24392.50 24392.50 24904.00 25982.00 22859.00 19731.00 17169.50 17169.50 27009.50 28032.00 20242.00 11429.50 16091.50 16091.50 19731.00 21831.50 21320.00 16091.50 18708.50 17681.00 26493.50 24392.50 16091.50 9891.00 15580.00 17169.50 18708.50 20753.50 16602.50 13019.00 14558.00 15069.00 28543.50 28543.50 20753.50 19731.00 22859.00 21320.00 25471.00 30644.00 31671.50 24392.50 22342.50 20242.00 34283.50 35311.00 32694.00 35311.00 34794.50 30644.00 35311.00 40484.00 42584.50 36333.50 32182.50 35311.00 48785.00 52936.00 51913.50 53958.00 53447.00 48274.00 57086.50 59187.00 62259.50 52936.00 49296.50 53447.00 63337.50 72150.00 70049.50 70049.50 71077.00 67488.00 76817.00 76817.00 75227.50 66410.00 67999.50 69027.00 75789.50 84602.00 78350.50 81479.00 78862.00 77328.00 89269.00 86652.00 83012.50 77839.50 83012.50 85629.50 88752.50 92903.00 88752.50 91314.00 88190.50 87679.50 100642.50 101720.50 97570.00 90291.50 96492.00 99104.00 102232.00 106894.00 102232.00 105871.50 104282.00 98592.50 110533.00 110533.00
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 Output
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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