Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data:
10.93 10.92 10.89 10.94 10.98 10.99 11.02 11.04 11.05 11.05 11.02 10.91 11.01 11.02 11.03 11.04 11.06 11.08 11.06 11.06 11.09 11.07 11.06 11.08 11.08 11.08 11.11 11.09 11.08 11.05 11.07 11.06 11.06 11.07 11.02 11.01 11.04 11.02 11.03 11.17 11.19 11.15 11.13 11.06 11.01 11.03 10.99 10.94 11 11.06 11.06 11.05 11.04 11.15 11.2 11.16 11.3 11.23 11.25 11.25 11.12 11.14 11.17 11.25 11.27 11.34 11.39 11.44 11.46 11.49 11.51 11.48 11.49 11.52 11.56 11.58 11.58 11.58 11.6 11.62 11.62 11.64 11.67 11.66 11.72 11.82 11.9 12.04 12.08 12.15 12.19 12.22 12.23 12.25 12.26 12.27 12.34 12.38 12.42 12.43 12.48 12.5 12.5 12.49 12.46 12.45 12.45 12.38 12.42 12.37 12.35 12.35 12.36 12.32 12.32 12.34 12.35 12.34 12.31 12.24
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$trend)) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,x[i]) if (par1 == 'additive') a<-table.element(a,m$trend[i]+m$seasonal[i]) else a<-table.element(a,m$trend[i]*m$seasonal[i]) a<-table.element(a,m$trend[i]) a<-table.element(a,m$seasonal[i]) a<-table.element(a,m$random[i]) 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