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Data:
3207324 3178086 3148485 3087246 3693267 3661164 3207324 2905563 2934699 2934699 2967168 3025542 3207324 3148485 3239325 3388641 4238049 4238049 4056732 3874950 4024266 4205949 4238049 4328892 4601499 4419732 4419732 4692342 5448042 5509281 5357202 4993734 5266260 5266260 5295495 5448042 5568120 5629359 5629359 5811042 6508356 6689670 6718809 6264966 6508356 6417513 6235746 6628434 6718809 6566727 6598830 6809748 7597920 7990056 7990056 7808739 8080980 7808739 7656276 8233527 8323902 8110134 8654901 8868684 9504309 9926133 9867774 9835290 10078677 10049076 9686076 10230759 10412541 10230759 10986462 11349927 12196086 12529950 12439476 12257691 12409872 12591555 11985168 12468612 12773337 12650394 13438098 13710240 14861511 15072414 14800254 14952351 15043194 15134034 14556783 15101568 15403311 15101568 15980562 16252821 17433111 17614893 17673267 17977893 17977893 18097971 17553189 17825814 18007131 17673267 18642738 18824436 20034063 20247849 20549592 20822217 20851353 20883456 20338689 20883456
Type of Seasonality
12
additive
multiplicative
Seasonal Period
Triple
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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