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Data:
369.07 369.32 370.38 371.63 371.32 371.51 369.69 368.18 366.87 366.94 368.27 369.62 370.47 371.44 372.39 373.32 373.77 373.13 371.51 369.59 368.12 368.38 369.64 371.11 372.38 373.08 373.87 374.93 375.58 375.44 373.91 371.77 370.72 370.5 372.19 373.71 374.92 375.63 376.51 377.75 378.54 378.21 376.65 374.28 373.12 373.1 374.67 375.97 377.03 377.87 378.88 380.42 380.62 379.66 377.48 376.07 374.1 374.47 376.15 377.51 378.43 379.7 380.91 382.2 382.45 382.14 380.6 378.6 376.72 376.98 378.29 380.07 381.36 382.19 382.65 384.65 384.94 384.01 382.15 380.33 378.81 379.06 380.17 381.85 382.88 383.77 384.42 386.36 386.53 386.01 384.45 381.96 380.81 381.09 382.37 383.84 385.42 385.72 385.96 387.18 388.5 387.88 386.38 384.15 383.07 382.98 384.11 385.54 386.92 387.41 388.77 389.46 390.18 389.43 387.74 385.91 384.77 384.38 385.99 387.26 388.45 389.7 391.08 392.46 392.96 392.03 390.13 388.15 386.8 387.18 388.59
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$trend)) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,x[i]) if (par1 == 'additive') a<-table.element(a,m$trend[i]+m$seasonal[i]) else a<-table.element(a,m$trend[i]*m$seasonal[i]) a<-table.element(a,m$trend[i]) a<-table.element(a,m$seasonal[i]) a<-table.element(a,m$random[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab')
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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