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Data:
4981.8 4938 4915.6 4938.4 4977.6 5045 5034.8 5094.8 5128.8 5181.8 5232 5384.4 5340.4 5452.6 5541.2 5627.2 5712 5794.2 5898.6 6021 6117.2 6245.4 6305.8 6327.2 6374.2 6383.2 6380 6373.4 6385 6399 6374.6 6342 6287.8 6198.8 6113.6 6110 6059 6004.6 5989.6 5988 5963 5972.2 5962.2 5930.4 5901.2 5860.8 5804.8 5727.6 5744.2 5744.8 5763.8 5774.6 5797.6 5806.8 5806.2 5809 5845.6 5876.8 5925.8 5972.2 5955.2 5972.4 5979.6 5976.4 5990.4 6029.4 6046.2 6094.6 6114.6 6149.4 6266.2 6312 6338 6417 6392.6 6455.4 6445.2 6415 6490.8 6552.8 6522.8 6568.8 6586.2 6627.6 6643.2 6635.4 6628.2 6628.8 6623.4 6596 6625 6613 6608.2 6590.2 6602.8 6525 6487.6 6449 6461.8 6476.8 6436.8 6399.4 6411.2 6356 6356.8 6337 6334.6 6310.6 6320.8 6321.8 6305.8 6293.8 6281.4 6306.8 6281.2 6325.8
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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0 seconds
R Server
Big Analytics Cloud Computing Center
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