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Data:
247832.00 246909.00 245973.00 244036.00 263198.00 262184.00 247832.00 238290.00 239213.00 239213.00 240240.00 242086.00 244959.00 244959.00 243113.00 238290.00 263198.00 266994.00 261261.00 247832.00 253578.00 244959.00 248846.00 250705.00 252642.00 247832.00 248846.00 242086.00 263198.00 269867.00 264134.00 253578.00 265057.00 252642.00 264134.00 263198.00 266071.00 255515.00 266994.00 266071.00 283296.00 279409.00 264134.00 256438.00 266994.00 252642.00 263198.00 265057.00 268944.00 260338.00 265057.00 267930.00 278486.00 269867.00 258388.00 245973.00 257465.00 225875.00 241163.00 249769.00 258388.00 245973.00 245973.00 245973.00 252642.00 243113.00 230607.00 220142.00 227734.00 198094.00 216255.00 226811.00 228748.00 218192.00 219115.00 216255.00 225875.00 219115.00 205790.00 196157.00 212446.00 177073.00 200044.00 210509.00 210509.00 198094.00 186615.00 185692.00 196157.00 186615.00 168467.00 155961.00 169390.00 137813.00 166517.00 181792.00 186615.00 176059.00 162721.00 172263.00 176059.00 173186.00 144469.00 131144.00 140673.00 111969.00 141609.00 152165.00 160771.00 146419.00 132990.00 140673.00 144469.00 136877.00 108173.00 95667.00 107146.00 75569.00 110019.00 131144.00
Type of Seasonality
additive
multiplicative
Seasonal Period
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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Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
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