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Data:
36439.00 36368.00 36290.00 36147.00 37615.00 37543.00 36439.00 35705.00 35777.00 35777.00 35848.00 35998.00 35998.00 35335.00 35043.00 35335.00 36368.00 36218.00 34822.00 33640.00 33419.00 32977.00 33276.00 33640.00 33497.00 33198.00 32614.00 33198.00 33718.00 33568.00 31873.00 31139.00 30405.00 29814.00 29743.00 30184.00 29593.00 29372.00 29151.00 30405.00 30548.00 29814.00 27826.00 26943.00 25547.00 24955.00 25247.00 25689.00 25689.00 25326.00 25247.00 26430.00 27385.00 26943.00 25468.00 24735.00 23189.00 22234.00 22968.00 23702.00 23702.00 22747.00 22676.00 23922.00 24735.00 24442.00 22968.00 22013.00 19947.00 19142.00 19434.00 20688.00 20759.00 18921.00 19584.00 21201.00 21935.00 21493.00 19506.00 18109.00 16492.00 15238.00 15751.00 16855.00 16563.00 14946.00 15459.00 17076.00 17960.00 17447.00 15459.00 14576.00 13251.00 11854.00 12075.00 13179.00 13322.00 11997.00 12218.00 14063.00 14504.00 13764.00 11042.00 9646.00 7801.00 5963.00 6554.00 7359.00 7217.00 5813.00 6625.00 8613.00 9496.00 9055.00 7288.00 5892.00 4417.00 2721.00 3021.00 3534.00
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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Big Analytics Cloud Computing Center
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