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Data:
2802 17.88 -5 5392 2706 18.62 -1 3122 2484 17.39 -2 1873 2884 17.17 -5 2206 3472 18.96 -4 1470 2726 17.22 -6 1984 3003 21.87 -2 2656 2289 21.61 -2 3322 2873 19.63 -2 4683 2589 18.23 -2 5785 2492 16.21 2 5438 2324 16.49 1 5614 2425 18.64 -8 6029 2509 17.67 -1 2859 2086 20.77 1 2056 2311 20.81 -1 2709 2381 17.78 2 1656 2300 19.28 2 2156 2593 21.29 1 2799 2337 17.37 -1 2949 2239 16.12 -2 4508 2765 15.15 -2 6056 2445 20.24 -1 6423 2172 22.95 -8 5854 1941 18.08 -4 5851 2581 18.46 -6 3330 2090 21.65 -3 1860 2085 21.39 -3 2524 2610 19.16 -7 1239 2481 24.21 -9 2302 2216 16.58 -11 2552 2820 15.59 -13 3424 2109 21.39 -11 5182 2671 20.47 -9 5054 2314 24.72 -17 4776 2200 19.14 -12 8179 2524 22.63 -25 5139 2181 19.32 -20 3612 1897 17.93 -24 1943 2123 20.62 -24 2301 1827 17.06 -22 1554 1784 14.63 -19 2069 2263 15.89 -18 2362 1982 15.32 -17 3069 1850 17.23 -11 5094 2569 18.88 -11 5059 2119 17.03 -12 4994 2407 19.88 -10 6073 2211 16.26 -15 5784 2131 15.62 -15 3483 1865 15.07 -15 1788 1889 20.65 -13 1896 2083 16.79 -8 1185 2178 17.28 -13 1758 2959 21.46 -9 1942 3294 20.57 -7 3310 3351 21.32 -4 4870 3599 19.91 -4 4755 2334 18.01 -2 5851 1672 21.87 0 5567 1364 19.11 -2 5259 1534 23.22 -3 3671 1444 21.38 1 1604 1701 20.52 -2 1918 1823 18.41 -1 1114 1783 21.35 1 1629 2107 21.68 -3 2023 1845 19.75 -4 3459 2272 16.36 -9 4340 1978 23.56 -9 5419 1801 19.57 -11 5745 2183 22.85 -14 5134 2117 21.48 -12 5051 1836 19.65 -16 3000 1963 23.66 -20 1672 2340 20.36 -12 2133 2522 23.19 -12 1348 2254 20.81 -10 1613 2573 20.25 -10 2342 2273 22.32 -13 3183 2060 18.85 -16 4821 2112 17.75 -14 5872 2082 19.78 -17 4399 1930 17.26 -24 5293 1871 19.23 -25 5425 2004 21.46 -23 2765 1795 19.19 -17 1850 1712 18.11 -24 2648 2170 16.01 -20 1180 1853 19.18 -19 1351 2124 21.59 -18 2307 2167 18.51 -16 2598 1832 21.01 -12 4136 2018 20.09 -7 5214 2146 17.63 -6 4075 1832 20.81 -6 5524 2005 18.97 -5 5020 2080 19.14 -4 2653 1792 17.97 -4 1817 2251 16.89 -8 2030 3007 19.62 -9 1025 3153 19.36 -6 1829 3750 19.27 -7 2135 3059 15.34 -10 2739 1614 15.02 -11 4735 1545 17.88 -11 5017 1428 19.09 -12 4365 1659 17.89 -14 5825 1825 15.62 -12 4942 1696 17.35 -9 2850 1508 19.96 -5 1736 1816 17.96 -6 2349
Type of Seasonality
additive
additive
multiplicative
Seasonal Period
1
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')
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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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