Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data:
63.2 68.6 77.7 68.1 75.1 73.3 60.5 65.9 77.7 67.7 67.7 67.7 67.7 67.7 67.7 67.7 67.7 67.7 67.7 67.7 67.7 67.7 67.7 67.7 73.2 72.2 79.3 77.5 75.6 77.4 69.2 67.1 77.9 82.7 75.7 70.1 76.4 74.3 80.5 78 73.5 78.8 71.2 66.2 82.7 83.8 75 80.4 74.6 77.7 89.8 82.4 77 89.6 75.7 75.1 89.9 88.8 86.5 90 84 82.7 91.7 87.5 82 92.2 73.1 75.6 91.6 87.5 90.1 91.3 87.6 88.4 100.7 85.3 92 96.8 77.9 80.9 95.3 99.3 96.1 92.5 93.7 92.1 103.6 92.5 95.7 103.4 89 89.1 98.7 109.4 101.1 95.4 101.4 102.1 103.6 106 98.4 106.6 95.8 87.2 108.5 107 92 94.9 84.4 85 94 84.5 88.2 92.1 81.1 81.2 96.1 95.3 92.1 91.7 90.3 96.1 108.7 95.9 95.1 109.4 91.2 91.4 107.4 105.6 105.3 103.7 99.5 103.2 123.1 102.2 110 106.2 91.3 99.3 111.8 104.4 102.4 101 100.6 104.5 117.4 97.4 99.5 106.4 95.2 94 104.1 105.8 101.1 93.5 97.9 96.8 108.4 103.5 101.3 107.4 100.7 91.1 105 112.8 105.6 101 101.9 103.5 109.5 105 102.9 108.5 96.9 88.4 112.4 111.3 101.6 101.2 101.8 98.8 114.4 104.5 97.6 109.1 94.5 90.4 111.8 110.5 106.8 101.8 103.7 107.4 117.5 109.6 102.8 115.5 97.8 100.2 112.9 108.7 109 113.9 106.9 109.6 124.5 104.2 110.8 118.7 102.1 105.1
Type of Seasonality
additive
multiplicative
Seasonal Period
12
1
2
3
4
5
6
7
8
9
10
11
12
Chart options
R Code
par2 <- '12' par1 <- 'additive' 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')
Compute
Summary of computational transaction
Raw Input
view raw input (R code)
Raw Output
view raw output of R engine
Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
Click here to blog (archive) this computation