Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data:
5221.3 5115.9 5107.4 5202.1 5307.5 5266.1 5329.8 5263.4 5177.1 5204.9 5185.2 5189.8 5253.8 5372.3 5478.4 5590.5 5699.8 5797.9 5854.3 5902.4 5956.9 6007.8 6101.7 6148.6 6207.4 6232 6291.7 6323.4 6365 6435 6493.4 6606.8 6639.1 6723.5 6759.4 6848.6 6918.1 6963.5 7013.1 7030.9 7112.1 7130.3 7130.8 7076.9 7040.8 7086.5 7120.7 7154.1 7228.2 7297.9 7369.5 7450.7 7459.7 7497.5 7536 7637.4 7715.1 7815.7 7859.5 7951.6 7973.7 7988 8053.1 8112 8169.2 8303.1 8372.7 8470.6 8536.1 8665.8 8773.7 8838.4 8936.2 8995.3 9098.9 9237.1 9315.5 9392.6 9502.2 9671.1 9695.6 9847.9 9836.6 9887.7 9875.6 9905.9 9871.1 9910 9977.3 10031.6 10090.7 10095.8 10126 10212.7 10398.7 10467 10543.6 10634.2 10728.7 10796.4 10875.8 10946.1 11050 11086.1 11217.3 11291.7 11314.1 11356.4 11357.8 11491.4 11625.7 11620.7
Type of Seasonality
multiplicative
additive
multiplicative
Seasonal Period
4
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