Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data:
3221816.00 3209817.00 3197649.00 3172468.00 3421574.00 3408392.00 3221816.00 3097770.00 3109769.00 3109769.00 3123120.00 3147118.00 3184467.00 3184467.00 3160469.00 3097770.00 3421574.00 3470922.00 3396393.00 3221816.00 3296514.00 3184467.00 3234998.00 3259165.00 3284346.00 3221816.00 3234998.00 3147118.00 3421574.00 3508271.00 3433742.00 3296514.00 3445741.00 3284346.00 3433742.00 3421574.00 3458923.00 3321695.00 3470922.00 3458923.00 3682848.00 3632317.00 3433742.00 3333694.00 3470922.00 3284346.00 3421574.00 3445741.00 3496272.00 3384394.00 3445741.00 3483090.00 3620318.00 3508271.00 3359044.00 3197649.00 3347045.00 2936375.00 3135119.00 3246997.00 3359044.00 3197649.00 3197649.00 3197649.00 3284346.00 3160469.00 2997891.00 2861846.00 2960542.00 2575222.00 2811315.00 2948543.00 2973724.00 2836496.00 2848495.00 2811315.00 2936375.00 2848495.00 2675270.00 2550041.00 2761798.00 2301949.00 2600572.00 2736617.00 2736617.00 2575222.00 2425995.00 2413996.00 2550041.00 2425995.00 2190071.00 2027493.00 2202070.00 1791569.00 2164721.00 2363296.00 2425995.00 2288767.00 2115373.00 2239419.00 2288767.00 2251418.00 1878097.00 1704872.00 1828749.00 1455597.00 1840917.00 1978145.00 2090023.00 1903447.00 1728870.00 1828749.00 1878097.00 1779401.00 1406249.00 1243671.00 1392898.00 982397.00 1430247.00 1704872.00
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')
Compute
Summary of computational transaction
Raw Input
view raw input (R code)
Raw Output
view raw output of R engine
Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
Click here to blog (archive) this computation