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Data:
98.6 98.8 99.9 100.3 100.2 100.2 100.6 100.4 100.7 100.9 99.7 99.7 96.8 99.2 99.9 99.3 98.9 98.9 98.7 98.4 98.6 98.5 98.1 98.3 98.1 97.9 99.1 98.5 98.2 97.8 98 98 97.6 97.6 97.6 97.5 96.1 96.1 96.3 96.3 96.3 96 96 95.2 96 96.1 95.3 95.1 94.8 94.5 94.7 94.8 94.5 94.5 92.8 92.8 94.5 94.4 94.2 94.1 92.9 93.3 93.6 93.6 94 94 94.2 93.3 93 93 94.7 95.6 95.8 96 95.4 95.3 94.4 94.4 94.3 93.9 94.5 93.6 93.9 93.9 93.7 94.6 94.4 94 91.1 91.1 90.7 90.8 89.8 90.7 90.3 89.7 89 88.4 89.3 89.3 89.3 89.3 88.4 89.4 91.3 90.9 91 89.3 88.1 89 90.1 90.6 90.6 90.2 89.5 90.5 90.4 89.7 90 90.2 89.3 89.6 89.8 89.4 89.3 89.4 89.5 89.2 90 88 88.3 89.1
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
0 seconds
R Server
Big Analytics Cloud Computing Center
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