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Data:
284.4 212.8 226.9 308.4 262 227.9 236.1 320.4 271.9 232.8 237 313.4 261.4 226.8 249.9 314.3 286.1 226.5 260.4 311.4 294.7 232.6 257.2 339.2 279.1 249.8 269.8 345.7 293.8 254.7 277.5 363.4 313.4 272.8 300.1 369.5 330.8 287.8 305.9 386.1 335.2 288 308.3 402.3 352.8 316.1 324.9 404.8 393 318.9 327 442.3 383.1 331.6 361.4 445.9 386.6 357.2 373.6 466.2 409.6 369.8 378.6 487 419.2 376.7 392.8 506.1 458.4 387.4 426.9 565 464.8 444.5 449.5 556.1 499.6 451.9 434.9 553.8 510 432.9 453.2 547.6 485.8 452.6 456.6 565.7 514.8 464.3 430.9 588.3 503.1 442.6 448 554.5 504.5 427.3 473.1 526.2 547.5 440.2 468.7 574.5 492.6 432.6 479.8 575.7 474.6 405.3 434.6 535.1 452.6 429.5 417.2 551.8 464 416.6 422.9 553.6 458.6 427.6 429.2 534.2 481.7 416 440.2 538.7 473.8 439.9 446.8 597.5 467.2 439.4 447.4 568.5 485.9 442.1 430.5 600 464.5 423.6 437 574 443 410 420 532 432 420 411 512
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')
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Big Analytics Cloud Computing Center
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