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Data:
22.2 23.2 23.3 23.4 23.8 23.5 23.6 23.4 23.6 24.0 24.6 24.7 24.4 25.1 25.6 25.6 26.1 26.9 26.4 27.9 26.1 27.4 27.2 27.9 27.1 29.0 29.5 29.4 30.2 36.0 39.0 41.3 40.2 38.8 39.3 38.7 39.6 40.8 48.1 51.8 52.6 65.0 67.5 63.6 57.3 54.9 54.3 58.9 65.9 82.7 100.1 100.7 97.5 92.3 85.1 91.6 93.6 90.4 99.3 107.7 106.2 98.8 99.6 98.9 92.7 91.8 92.6 98.4 94.6 85.8 84.6 83.5 84.7 80.1 84.4 85.9 87.1 84.5 83.1 75.9 70.1 78.1 83.1 87.9 90.2 89.9 97.1 102.1 100.6 97.7 97.6 100.3 102.0 107.8 111.5 110.2 110.1 117.4 119.8 118.8 113.2 122.8 120.4 129.2 132.5 135.7 141.5 122.4 137.1 144.8 154.6 148.0 152.8 172.0 169.0 179.7 190.4 233.2 231.4 244.9 299.1 385.0 381.5 321.6 317.3 323.1 392.7 372.4 386.5 412.8 404.9 406.7 392.4 363.3 357.9 375.1 369.7 386.1 353.4 346.9 362.5 349.9 347.0 332.9 327.5 327.9 308.9 285.7 318.8 284.8 301.0 315.2 388.3 383.4 416.8 423.2 429.9 486.1 394.4 410.9 430.9 447.3 431.7 456.5 452.9 440.9 416.5 451.5 432.0 436.2 428.6 421.4 425.2 437.2 431.9 412.7 419.4 436.4 421.4 423.7 402.4 402.8 400.5 425.7 417.9 403.4 405.0 393.6 400.0 375.9 366.6 353.9 347.5 364.1 328.6 348.0 329.6 351.0 336.2 332.2 349.5 383.6 369.8 345.5 337.8 334.8 338.0 346.7 371.8 375.9 373.3 391.9 374.3 384.7 372.2 372.0 351.8 352.9 330.5 347.7 345.6 360.8 364.4 374.6 369.1 341.8 337.9 336.6 332.7 335.7 321.6 329.4 321.8 324.6 330.9 310.9 318.1 312.4 315.2 332.9 310.7 321.3 316.2 283.9 280.6 280.2 265.9 267.8 278.0 291.9 262.6 264.8 265.7 251.1 256.1 279.7 282.5 288.9 308.5 292.9 280.8 273.6 276.7 277.9 250.3 264.7 268.9 261.7 258.0 251.3 243.1 246.8 224.5 241.2 255.0 261.4 266.7 264.3 270.4 275.0 281.1 300.7 321.1 354.8 319.0 298.7 318.9 327.9 348.2 335.2 333.0 331.0 317.5 325.3 317.6 313.4 313.0 314.8 298.4 311.1 308.8 297.3 293.6 291.3 291.5 289.9 287.1 280.7 294.9 289.0 285.6 294.6 290.7 314.8 306.5 304.5 308.7 307.0 298.6 293.5 294.9 296.1 294.2 291.7 290.5 288.7 310.1 297.4 300.8 301.6 296.9 305.2 298.5 298.7 273.0 266.6 266.1 284.5 275.7 284.2 284.8 267.3 273.0 262.3 246.3 251.0 247.5 254.8 245.1 251.3 261.5 258.8 270.9 257.6 253.1 238.8 241.2 280.8 284.6 289.4 289.6 289.6 305.0 289.2 301.8 293.6 300.6 298.7 311.6 310.1 312.1 309.1 292.3 284.4 290.0 291.5 296.8 315.6 319.6 303.9 300.5 321.8 309.5 307.7 310.5 327.9 343.2 345.5 342.0 349.6 322.5 310.7 319.0 327.5 320.0 320.7 330.9 342.3 322.4 306.9 301.7 307.3 301.3 315.2 342.1 333.2 332.3 332.3 330.0 321.8 318.6 344.8 324.1 322.0 325.3 325.1 335.1 334.7 334.5 341.1 320.5 323.8 328.1 328.9 337.5 335.7 361.0 353.2 352.3 392.5 393.0 420.4 434.9 468.4 466.3 480.9 511.3 508.4 479.8 495.6 487.1 473.1 473.0 487.9 479.3 500.6 502.8 497.1 496.1 489.8 481.7 486.2 492.9 522.4 545.7 533.8 570.3 623.6 639.9 589.1 559.4 570.0 590.4 588.4 565.8 629.7 576.3 641.9 625.7 717.5 749.6 690.3 666.6 689.2 666.2 662.3 665.8 681.2 704.9 783.1 758.0 775.9 812.1 824.4 886.9 984.1 1.015.6 897.3 980.4 957.4 969.0 1.062.8 1.047.7 967.9 1.021.6 1.014.0 1.035.0 1.068.8 1.038.4 1.133.3 1.259.6 1.207.4 1.234.6 1.297.0 1.179.4 1.332.3 1.323.2 1.248.4 1.247.6 1.260.1 1.259.6 1.317.0 1.307.8 1.380.5 1.326.6 1.327.1
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$trend)) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,x[i]) if (par1 == 'additive') a<-table.element(a,m$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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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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