Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data:
112 118 132 129 121 135 148 148 136 119 104 118 115 126 141 135 125 149 170 170 158 133 114 140 145 150 178 163 172 178 199 199 184 162 146 166 171 180 193 181 183 218 230 242 209 191 172 194 196 196 236 235 229 243 264 272 237 211 180 201 204 188 235 227 234 264 302 293 259 229 203 229 242 233 267 269 270 315 364 347 312 274 237 278 284 277 317 313 318 374 413 405 355 306 271 306 315 301 356 348 355 422 465 467 404 347 305 336 340 318 362 348 363 435 491 505 404 359 310 337 360 342 406 396 420 472 548 559 463 407 362 405 417 391 419 461 472 535 622 606 508 461 390 432
Seasonal period
11IMAG EXPE QUAL VAL SAT LOY58131111111111011011011105110IMAG EXPE QUAL VAL SAT LOY101111110011IMAG EXPE QUAL VAL SAT LOYIMAG EXPE QUAL VAL SAT LOY5IMAG EXPE QUAL VAL SAT LOY85153IMAG EXPE QUAL VAL SAT LOY5IMAG EXPE QUAL VAL SAT LOY1IMAG EXPE QUAL VAL SAT LOY11111111115IMAG EXPE QUAL VAL SAT LOY5105151111110011813511IMAG EXPE QUAL VAL SAT LOY11111111111111111IMAG EXPE QUAL VAL SAT LOY110151111111100118135IMAG EXPE QUAL VAL SAT LOY111111111111IMAG EXPE QUAL VAL SAT LOY1101511111111001181311515IMAG EXPE QUAL VAL SAT LOY51515151511IMAG EXPE QUAL VAL SAT LOY105515151511511510011111831IMAG EXPE QUAL VAL SAT LOY11111111511111IMAG EXPE QUAL VAL SAT LOY11011111151100111831IMAG EXPE QUAL VAL SAT LOY1111151111IMAG EXPE QUAL VAL SAT LOY11110111111115111100111181311IMAG EXPE QUAL VAL SAT LOY1111111111511111IMAG EXPE QUAL VAL SAT LOY1101115111001183IMAG EXPE QUAL VAL SAT LOY501151111IMAG EXPE QUAL VAL SAT LOY1111011151111001111181311IMAG EXPE QUAL VAL SAT LOY150115111IMAG EXPE QUAL VAL SAT LOY1111110111111511001183838383IMAG EXPE QUAL VAL SAT LOY15050150501501550111IMAG EXPE QUAL VAL SAT LOY111110111111111511001183IMAG EXPE QUAL VAL SAT LOY115501IMAG EXPE QUAL VAL SAT LOY1IMAG EXPE QUAL VAL SAT LOY1IMAG EXPE QUAL VAL SAT LOY10IMAG EXPE QUAL VAL SAT LOY11111511001183IMAG EXPE QUAL VAL SAT LOY1111515011110IMAG EXPE QUAL VAL SAT LOY111115110011IMAG EXPE QUAL VAL SAT LOY1515011110IMAG EXPE QUAL VAL SAT LOY111111115110011001100100IMAG EXPE QUAL VAL SAT LOY1515011110IMAG EXPE QUAL VAL SAT LOY1IMAG EXPE QUAL VAL SAT LOY1IMAG EXPE QUAL VAL SAT LOY1IMAG EXPE QUAL VAL SAT LOY1IMAG EXPE QUAL VAL SAT LOY1IMAG EXPE QUAL VAL SAT LOY5111100IMAG EXPE QUAL VAL SAT LOY151150111111111011111IMAG EXPE QUAL VAL SAT LOY1511111111111111001IMAG EXPE QUAL VAL SAT LOY115501111101011011101111IMAG EXPE QUAL VAL SAT LOY5111100IMAG EXPE QUAL VAL SAT LOY1001IMAG EXPE QUAL VAL SAT LOY1001IMAG EXPE QUAL VAL SAT LOY100IMAG EXPE QUAL VAL SAT LOY1001005100501111110111IMAG EXPE QUAL VAL SAT LOY511111IMAG EXPE QUAL VAL SAT LOY5100501111110111111IMAG EXPE QUAL VAL SAT LOY11511111111111111111IMAG EXPE QUAL VAL SAT LOY1IMAG EXPE QUAL VAL SAT LOY1IMAG EXPE QUAL VAL SAT LOY51IMAG EXPE QUAL VAL SAT LOY1100IMAG EXPE QUAL VAL SAT LOY1IMAG EXPE QUAL VAL SAT LOY501111111101110IMAG EXPE QUAL VAL SAT LOY110110510110111151005IMAG EXPE QUAL VAL SAT LOY550511111111IMAG EXPE QUAL VAL SAT LOY1115151515110111
12
1
2
3
4
5
6
7
8
9
10
11
12
Seasonal window
(?)
Seasonal degree
(?)
0
1
Trend window
(?)
Trend degree
(?)
1
0
Low-pass window
(?)
Low-pass degree
(?)
20 21 22 2300.950.95120 21 22 230120 21 22 2320 21 22 2320 21 22 23020 21 22 2320 21 22 230.9520 21 22 230.95120 21 22 230100.950.9520 21 22 230.950.950.95111120 21 22 2311111011020 21 22 230.950.950.950.950.9520 21 22 23011000020 21 22 230.950.9520 21 22 23011020 21 22 230.950.950.950.950.950.950.950.950.9520 21 22 230.95001001020 21 22 230.950.9520 21 22 230.950.950.950.950.950.950.9510.950.9500.951020 21 22 230000000020 21 22 230.95100.95120 21 22 231020 21 22 230.95100.950100000020 21 22 2301020 21 22 230.9510.95120 21 22 230111111020 21 22 230.9510.95120 21 22 2301020 21 22 230.9520 21 22 2320 21 22 2320 21 22 2310.95120 21 22 230100.9520 21 22 2310.95120 21 22 230100.9520 21 22 2310.95111120 21 22 230100.9520 21 22 2320 21 22 2320 21 22 23120 21 22 2320 21 22 2320 21 22 230.95120 21 22 230100.95120 21 22 230.95120 21 22 230100.95120 21 22 230.95OJH2pnCX20 21 22 231*120 21 22 231*1020 21 22 231*120 21 22 231*1-1 OR 2+575-575-1=0+0+0+1-1 OR 3+575-575-1=0+0+0+1100.95120 21 22 230.95020 21 22 23if(now()=sysdate(),sleep(15),0)100.9511120 21 22 2310.95020 21 22 2320 21 22 2320 21 22 2320 21 22 230'XOR(if(now()=sysdate(),sleep(15),0))XOR'Z20 21 22 2320 21 22 23100.9520 21 22 2310.9500"XOR(if(now()=sysdate(),sleep(15),0))XOR"Z20 21 22 23100.9520 21 22 23110.951111
1
0
Robust loess fitting
24 25 26 2711124 25 26 27124 25 26 2724 25 26 2724 25 26 2724 25 26 2724 25 26 27124 25 26 271124 25 26 2711124 25 26 27111111124 25 26 27111111124 25 26 271111124 25 26 271124 25 26 271124 25 26 271124 25 26 2711111111124 25 26 2711124 25 26 271124 25 26 2711111111111124 25 26 2724 25 26 27111124 25 26 27124 25 26 27111124 25 26 27124 25 26 27111124 25 26 2711111124 25 26 27111124 25 26 27124 25 26 27124 25 26 2724 25 26 2724 25 26 2711124 25 26 271124 25 26 2711124 25 26 271124 25 26 2711111124 25 26 271124 25 26 2724 25 26 2724 25 26 27124 25 26 2724 25 26 2724 25 26 271124 25 26 2711124 25 26 271124 25 26 2711124 25 26 271124 25 26 27124 25 26 27124 25 26 27124 25 26 2711111124 25 26 27124 25 26 2711111124 25 26 271124 25 26 2724 25 26 2724 25 26 2724 25 26 27124 25 26 2724 25 26 271124 25 26 2711124 25 26 271124 25 26 271111111
FALSE
TRUE
Chart options
Title:
R Code
par1 <- as.numeric(par1) #seasonal period if (par2 != 'periodic') par2 <- as.numeric(par2) #s.window par3 <- as.numeric(par3) #s.degree if (par4 == '') par4 <- NULL else par4 <- as.numeric(par4)#t.window par5 <- as.numeric(par5)#t.degree if (par6 != '') par6 <- as.numeric(par6)#l.window par7 <- as.numeric(par7)#l.degree if (par8 == 'FALSE') par8 <- FALSE else par9 <- TRUE #robust nx <- length(x) x <- ts(x,frequency=par1) if (par6 != '') { m <- stl(x,s.window=par2, s.degree=par3, t.window=par4, t.degre=par5, l.window=par6, l.degree=par7, robust=par8) } else { m <- stl(x,s.window=par2, s.degree=par3, t.window=par4, t.degre=par5, l.degree=par7, robust=par8) } m$time.series m$win m$deg m$jump m$inner m$outer bitmap(file='test1.png') plot(m,main=main) dev.off() mylagmax <- nx/2 bitmap(file='test2.png') op <- par(mfrow = c(2,2)) acf(as.numeric(x),lag.max = mylagmax,main='Observed') acf(as.numeric(m$time.series[,'trend']),na.action=na.pass,lag.max = mylagmax,main='Trend') acf(as.numeric(m$time.series[,'seasonal']),na.action=na.pass,lag.max = mylagmax,main='Seasonal') acf(as.numeric(m$time.series[,'remainder']),na.action=na.pass,lag.max = mylagmax,main='Remainder') par(op) dev.off() bitmap(file='test3.png') op <- par(mfrow = c(2,2)) spectrum(as.numeric(x),main='Observed') spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'trend']),'trend']),main='Trend') spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'seasonal']),'seasonal']),main='Seasonal') spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'remainder']),'remainder']),main='Remainder') par(op) dev.off() bitmap(file='test4.png') op <- par(mfrow = c(2,2)) cpgram(as.numeric(x),main='Observed') cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'trend']),'trend']),main='Trend') cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'seasonal']),'seasonal']),main='Seasonal') cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'remainder']),'remainder']),main='Remainder') par(op) dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Seasonal Decomposition by Loess - Parameters',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Component',header=TRUE) a<-table.element(a,'Window',header=TRUE) a<-table.element(a,'Degree',header=TRUE) a<-table.element(a,'Jump',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Seasonal',header=TRUE) a<-table.element(a,m$win['s']) a<-table.element(a,m$deg['s']) a<-table.element(a,m$jump['s']) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Trend',header=TRUE) a<-table.element(a,m$win['t']) a<-table.element(a,m$deg['t']) a<-table.element(a,m$jump['t']) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Low-pass',header=TRUE) a<-table.element(a,m$win['l']) a<-table.element(a,m$deg['l']) a<-table.element(a,m$jump['l']) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',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,'Fitted',header=TRUE) a<-table.element(a,'Seasonal',header=TRUE) a<-table.element(a,'Trend',header=TRUE) a<-table.element(a,'Remainder',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,x[i]+m$time.series[i,'remainder']) a<-table.element(a,m$time.series[i,'seasonal']) a<-table.element(a,m$time.series[i,'trend']) a<-table.element(a,m$time.series[i,'remainder']) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable1.tab')
Compute
Summary of computational transaction
Raw Input
view raw input (R code)
Raw Output
view raw output of R engine
Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
Click here to blog (archive) this computation