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Data:
0.0314796223103059 -3.00870920563557 -2.07677512619799 -1.25010391965540 0.817975239137125 0.0252076485413113 0.554937772830776 0.230027371950115 2.35672227418686 1.41350455171120 2.73311719024401 1.31551925971717 -2.70076272244080 -0.721411049152714 -0.149388576811997 -0.118199629770334 -0.676562489695275 1.79699928690761 1.79845572032988 0.245100010770855 1.80710848932636 -1.75934771184948 -0.0186697168761931 0.189651523600062 -1.84149562719087 -1.07019530156943 -0.507291477584104 0.866365633831705 -1.76077926699189 -0.580719393339347 -0.435702079860853 -0.994868534845203 1.63136048315789 -1.1949403709466 -1.00525975426991 1.32302234837564 -0.628357549594746 0.632048410440518 -2.16903155809288 2.53779364144266 -0.632933703679292 -1.41749196342200 -0.455343045381255 0.812255211942954 0.627897309219833 0.650904313655623 -1.29800419154382 0.74391671726854 -1.50461634127457 -1.42734677658523 0.263353807408564 -0.430830854870631 0.379576092518008 1.70309353400146 -3.12314448117342 -1.32526207118689 -0.60032490743804 1.23607137604666 0.738007075905376 0.899100896289585
Seasonal period
12
12
1
2
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4
5
6
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8
9
10
11
12
Number of Forecasts
12
1
2
3
4
5
6
7
8
9
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11
12
13
14
15
16
17
18
Algorithm
BFGS
L-BFGS-B
Chart options
R Code
par1 <- as.numeric(par1) nx <- length(x) x <- ts(x,frequency=par1) m <- StructTS(x,type='BSM') m$coef m$fitted m$resid mylevel <- as.numeric(m$fitted[,'level']) myslope <- as.numeric(m$fitted[,'slope']) myseas <- as.numeric(m$fitted[,'sea']) myresid <- as.numeric(m$resid) myfit <- mylevel+myseas mylagmax <- nx/2 bitmap(file='test2.png') op <- par(mfrow = c(2,2)) acf(as.numeric(x),lag.max = mylagmax,main='Observed') acf(mylevel,na.action=na.pass,lag.max = mylagmax,main='Level') acf(myseas,na.action=na.pass,lag.max = mylagmax,main='Seasonal') acf(myresid,na.action=na.pass,lag.max = mylagmax,main='Standardized Residals') par(op) dev.off() bitmap(file='test3.png') op <- par(mfrow = c(2,2)) spectrum(as.numeric(x),main='Observed') spectrum(mylevel,main='Level') spectrum(myseas,main='Seasonal') spectrum(myresid,main='Standardized Residals') par(op) dev.off() bitmap(file='test4.png') op <- par(mfrow = c(2,2)) cpgram(as.numeric(x),main='Observed') cpgram(mylevel,main='Level') cpgram(myseas,main='Seasonal') cpgram(myresid,main='Standardized Residals') par(op) dev.off() bitmap(file='test1.png') plot(as.numeric(m$resid),main='Standardized Residuals',ylab='Residuals',xlab='time',type='b') grid() dev.off() bitmap(file='test5.png') op <- par(mfrow = c(2,2)) hist(m$resid,main='Residual Histogram') plot(density(m$resid),main='Residual Kernel Density') qqnorm(m$resid,main='Residual Normal QQ Plot') qqline(m$resid) plot(m$resid^2, myfit^2,main='Sq.Resid vs. Sq.Fit',xlab='Squared residuals',ylab='Squared Fit') par(op) dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Structural Time Series Model',6,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'t',header=TRUE) a<-table.element(a,'Observed',header=TRUE) a<-table.element(a,'Level',header=TRUE) a<-table.element(a,'Slope',header=TRUE) a<-table.element(a,'Seasonal',header=TRUE) a<-table.element(a,'Stand. Residuals',header=TRUE) a<-table.row.end(a) for (i in 1:nx) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,x[i]) a<-table.element(a,mylevel[i]) a<-table.element(a,myslope[i]) a<-table.element(a,myseas[i]) a<-table.element(a,myresid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab')
Compute
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Raw Input
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Raw Output
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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