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Data:
7263.63 7135.88 7008.00 6752.38 9339.00 9211.13 7263.63 5970.38 6098.13 6098.13 6226.00 6495.50 5714.75 4932.75 4292.38 4292.38 6752.38 7008.00 5060.50 2857.38 4022.88 4022.88 4932.75 5457.88 5330.00 4022.88 4677.13 4420.25 6623.38 6098.13 4022.88 2472.75 3895.00 4292.38 4677.13 5188.38 4150.63 3254.75 3639.50 3767.25 7135.88 7135.88 5188.38 4932.75 5714.75 5330.00 6367.75 7661.00 7917.88 6098.13 5585.63 5060.50 8570.88 8827.75 8173.50 8827.75 8698.63 7661.00 8827.75 10121.00 10646.13 9083.38 8045.63 8827.75 12196.25 13234.00 12978.38 13489.50 13361.75 12068.50 14271.63 14796.75 15564.88 13234.00 12324.13 13361.75 15834.38 18037.50 17512.38 17512.38 17769.25 16872.00 19204.25 19204.25 18806.88 16602.50 16999.88 17256.75 18947.38 21150.50 19587.63 20369.75 19715.50 19332.00 22317.25 21663.00 20753.13 19459.88 20753.13 21407.38 22188.13 23225.75 22188.13 22828.50 22047.63 21919.88 25160.63 25430.13 24392.50 22572.88 24123.00 24776.00 25558.00 26723.50 25558.00 26467.88 26070.50 24648.13 27633.25 27633.25
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$trend)) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,x[i]) if (par1 == 'additive') a<-table.element(a,signif(m$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
1 seconds
R Server
Big Analytics Cloud Computing Center
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