Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data:
3949.9 4010.65 4381.8 4238.25 4178.1 4702.25 3944.1 4208.5 4743.45 4948.25 4735.45 4843.15 4757.75 5227.15 5739.65 4981.45 5020.05 5149.15 4513.35 4762.55 4990.45 4963.35 5010 4983.3 4924.7 5175.25 5470.3 4969.4 5020.5 5519.2 4510.75 4934.45 5430.65 5254.7 4897.8 5305.7 5055.7 5409 5683 5125.55 4965.2 5373.3 4556.1 4714.25 5513.85 5258.45 5111.4 5422.25 4753.3 5455.5 5909.15 5524.4 5477.8 5907.75 5072.55 5171 5871.4 5812.45 5692.2 5838.1 5438.2 6041.05 6335.6 5891.8 5909.65 6449.75 5312.25 5828.1 6466.15 6328.35 6131.8 6734.2 6037.25 6412.4 6785.55 6386 6045.25 6597.25 5355.9 5773.35 6539.6 6149.2 6373.45 6504.7 5451.25 6119.9 6954.95 6139.7 6383.25 6643.7 5547.75 5974 6583.6 6571.55 5736.5 6027.2 5302.65 5825.85 5910.6 5733.65 5914.3 6128.25 5680.5 5926.3 6270.5 6263 6064.55 5706.6 5365 5884.2 6504.4 6174.3 6123.65 6698.95 5256.55 5838.2
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
0 seconds
R Server
Big Analytics Cloud Computing Center
Click here to blog (archive) this computation