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Data:
40927.00 40856.00 40778.00 40635.00 42103.00 42032.00 40927.00 40194.00 40265.00 40265.00 40336.00 40486.00 40856.00 40414.00 40856.00 40486.00 41661.00 42181.00 39973.00 39381.00 39894.00 39823.00 39381.00 39453.00 40336.00 40194.00 40336.00 40336.00 41298.00 41440.00 38790.00 38790.00 39823.00 39310.00 38427.00 38790.00 39674.00 39232.00 39161.00 38206.00 39602.00 39894.00 37023.00 36952.00 38427.00 37615.00 36218.00 36810.00 37465.00 37615.00 37173.00 36290.00 38128.00 38128.00 34893.00 34673.00 35556.00 33939.00 32314.00 32835.00 33939.00 33055.00 32464.00 31210.00 32906.00 32977.00 29743.00 29664.00 30256.00 28418.00 26430.00 27235.00 28339.00 27164.00 27093.00 25910.00 27826.00 28197.00 24585.00 23780.00 24293.00 22305.00 20246.00 20909.00 22156.00 20688.00 20909.00 20026.00 21864.00 22084.00 17668.00 17375.00 18180.00 16050.00 14134.00 14797.00 16414.00 14504.00 14355.00 12880.00 14504.00 15017.00 10451.00 10451.00 11113.00 9347.00 7359.00 8392.00 10230.00 8242.00 9055.00 7950.00 9717.00 10308.00 5592.00 5229.00 5963.00 4196.00 2800.00 3384.00
Type of Seasonality
multiplicative
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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Big Analytics Cloud Computing Center
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