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Data:
4.69 4.88 4.84 4.81 5.22 5.16 5.27 5.35 5.33 5.29 5.28 4.94 4.70 4.73 4.57 4.61 4.69 4.53 4.55 4.31 4.03 3.72 3.64 3.81 3.95 4.26 4.55 4.54 4.55 4.39 4.21 4.00 3.67 3.60 3.53 3.35 3.10 2.89 2.97 3.03 2.71 2.44 2.60 2.93 2.86 2.88 3.00 2.90 2.64 2.75 2.70 2.87 3.03 3.14 3.02 2.86 3.07 2.93 2.83 2.72 2.73 2.72 2.77 2.61 2.47 2.30 2.38 2.43 2.39 2.60 2.84 2.87 2.92 3.08 3.33 3.48 3.57 3.66 3.77 3.75 3.75 3.81 3.82 3.89 4.05 4.10 4.07 4.26 4.40 4.61 4.63 4.48 4.46 4.45 4.32 4.52 4.21 3.97 4.12 4.50 4.73 5.26 5.20 4.94 4.95 4.52 3.85 3.41 2.95 2.68 2.53 2.44 2.16 2.20 2.10 2.29 2.03 2.05 1.94 1.87 1.89 1.94 1.79 1.71 1.66 1.74 1.83 1.64 1.69 1.78 1.89 1.95 2.05 2.24 2.38 2.53 2.36 2.22 2.12 1.75 1.76 1.81 1.71 1.74 1.48 1.24 1.16 1.11 0.98 0.94 0.65 0.42 0.41 0.40
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$trend)) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,x[i]) if (par1 == 'additive') a<-table.element(a,m$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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Raw Output
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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