inflation in consumer prices (%)

*Unverified author*

R Software Module: /rwasp_decompose.wasp (opens new window with default values)

Title produced by software: Classical Decomposition

Date of computation: Thu, 19 May 2011 18:56:06 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2011/May/19/t1305831149rbz01ogfyes9to0.htm/, Retrieved Thu, 19 May 2011 20:52:30 +0200

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

KDGP2W92

Dataseries X:

» Textbox « » Textfile « » CSV «

0.440 0.548 0.163 0.381 0.164 0.109 0.328 0.435 0.325 0.108 0.054 0.270 0.431 0.215 0.214 0.160 0.427 0.372 0.106 0.053 0.317 0.527 0.472 0.000 0.052 0.418 0.364 0.311 0.052 0.052 0.620 0.616 1.377 0.151 0.502 0.000 0.606 0.050 0.150 0.501 0.299 0.248 0.545 0.444 0.491 0.444 0.050 0.545 0.138 0.423 0.495 0.370 0.388 0.169 0.241 0.014 0.376 0.331 0.789 0.289 0.359 0.236 0.367 0.309 0.551 0.901 0.870 0.160 0.032 0.877 1.812 0.784 0.270 0.462 0.146 0.108 0.132 0.680 0.117 0.345 0.204 0.227 0.236 0.092 0.138 0.046 0.023 0.009 0.142 0.207 0.346 0.207 0.165 0.247 0.123 0.433

Output produced by software:

Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 5 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 0.44 NA NA 0.858621255389303 NA 2 0.548 NA NA 0.799519448863304 NA 3 0.163 NA NA 0.762065669512621 NA 4 0.381 NA NA 0.745190278576668 NA 5 0.164 NA NA 0.889364616078144 NA 6 0.109 NA NA 1.1549413306375 NA 7 0.328 0.296648782086995 0.276708333333333 1.07206305828759 1.10568463383683 8 0.435 0.250096254433536 0.262458333333333 0.9528988897293 1.73933032697858 9 0.325 0.351306026750251 0.250708333333333 1.40125388765265 0.925119341123766 10 0.108 0.269740946292577 0.243625 1.10719731674737 0.400384151847887 11 0.054 0.366073415100813 0.245375 1.49189369373739 0.147511394634131 12 0.27 0.204475600373582 0.267291666666667 0.76499055478815 1.32045094625815 13 0.431 0.230969117699722 0.269 0.858621255389303 1.86605033734567 14 0.215 0.194949492281169 0.243833333333333 0.799519448863304 1.10284975602764 15 0.214 0.173433445286581 0.227583333333333 0.762065669512621 1.23390272070296 16 0.16 0.182354271086699 0.244708333333333 0.745190278576668 0.877412955816809 17 0.427 0.248651523911848 0.279583333333333 0.889364616078144 1.71726275102734 18 0.372 0.330024485229667 0.28575 1.1549413306375 1.12718909247331 19 0.106 0.277351647037819 0.258708333333333 1.07206305828759 0.382186300792172 20 0.053 0.239534958405703 0.251375 0.9528988897293 0.221262066934854 21 0.317 0.37285030527291 0.266083333333333 1.40125388765265 0.850207162276479 22 0.527 0.308492852378735 0.278625 1.10719731674737 1.70830538191207 23 0.472 0.401754539276032 0.269291666666667 1.49189369373739 1.17484671324573 24 0 0.183852730000752 0.240333333333333 0.76499055478815 0 25 0.052 0.213295830192959 0.248416666666667 0.858621255389303 0.243792857802039 26 0.418 0.234492391689533 0.293291666666667 0.799519448863304 1.7825738267595 27 0.364 0.275042201221597 0.360916666666667 0.762065669512621 1.32343327090642 28 0.311 0.290189514315731 0.389416666666667 0.745190278576668 1.07171343090511 29 0.052 0.333511731029304 0.375 0.889364616078144 0.15591655453772 30 0.052 0.434546675652361 0.37625 1.1549413306375 0.119664935698646 31 0.62 0.428110514609511 0.399333333333333 1.07206305828759 1.44822418240654 32 0.616 0.387909256360636 0.407083333333333 0.9528988897293 1.5880002601106 33 1.377 0.536446696656358 0.382833333333333 1.40125388765265 2.56689063160005 34 0.151 0.42276484211137 0.381833333333333 1.10719731674737 0.357172557788573 35 0.502 0.596819639732196 0.400041666666667 1.49189369373739 0.841125134932316 36 0 0.320148547178841 0.4185 0.76499055478815 0 37 0.606 0.363661877543011 0.423541666666667 0.858621255389303 1.66638308115848 38 0.05 0.33040141224276 0.41325 0.799519448863304 0.151331072287496 39 0.15 0.281329242995076 0.369166666666667 0.762065669512621 0.533183107461834 40 0.501 0.256687001374721 0.344458333333333 0.745190278576668 1.95179341889861 41 0.299 0.3004570127984 0.337833333333333 0.889364616078144 0.995150678012707 42 0.248 0.394653077189924 0.341708333333333 1.1549413306375 0.628400015947809 43 0.545 0.369772416521028 0.344916666666667 1.07206305828759 1.47387954225355 44 0.444 0.324898817277286 0.340958333333333 0.9528988897293 1.36657930527665 45 0.491 0.519690035583178 0.370875 1.40125388765265 0.944793947124688 46 0.444 0.420504314256344 0.379791666666667 1.10719731674737 1.05587501708563 47 0.05 0.563997978469974 0.378041666666667 1.49189369373739 0.0886528000253496 48 0.545 0.289517050380865 0.378458333333333 0.76499055478815 1.882445262837 49 0.138 0.311250205078622 0.3625 0.858621255389303 0.443373201843003 50 0.423 0.265373830401878 0.331916666666667 0.799519448863304 1.59397782124716 51 0.495 0.235637055560548 0.309208333333333 0.762065669512621 2.10068827596942 52 0.37 0.223339736408415 0.299708333333333 0.745190278576668 1.65666892040828 53 0.388 0.289747580546459 0.325791666666667 0.889364616078144 1.33909660011048 54 0.169 0.39951345528969 0.345916666666667 1.1549413306375 0.42301453871549 55 0.241 0.36928105428598 0.344458333333333 1.07206305828759 0.652619453944053 56 0.014 0.329583903485122 0.345875 0.9528988897293 0.0424778026231247 57 0.376 0.466267231116421 0.33275 1.40125388765265 0.806404514208973 58 0.331 0.359700728278301 0.324875 1.10719731674737 0.920209424051833 59 0.789 0.49101951195132 0.329125 1.49189369373739 1.6068607882088 60 0.289 0.280305289116958 0.366416666666667 0.76499055478815 1.03101871859226 61 0.359 0.363304118686599 0.423125 0.858621255389303 0.988152849182776 62 0.236 0.364114482336497 0.455416666666667 0.799519448863304 0.648147798147453 63 0.367 0.340770365217061 0.447166666666667 0.762065669512621 1.0769715839763 64 0.309 0.339496271081554 0.455583333333333 0.745190278576668 0.910171999873814 65 0.551 0.46332190811771 0.520958333333333 0.889364616078144 1.18923795820166 66 0.901 0.674726349869519 0.584208333333333 1.1549413306375 1.33535617835918 67 0.87 0.644443905913128 0.601125 1.07206305828759 1.3500011281312 68 0.16 0.578250809584064 0.606833333333333 0.9528988897293 0.276696543001968 69 0.032 0.850619495383813 0.607041666666667 1.40125388765265 0.0376196409483433 70 0.877 0.652646685001042 0.589458333333333 1.10719731674737 1.34375921942912 71 1.812 0.840868583132738 0.563625 1.49189369373739 2.15491461608569 72 0.784 0.410768053314787 0.536958333333333 0.76499055478815 1.90861970319584 73 0.27 0.426198125643865 0.496375 0.858621255389303 0.633508182590212 74 0.462 0.377939506139758 0.472708333333333 0.799519448863304 1.22241785390162 75 0.146 0.371570519359862 0.487583333333333 0.762065669512621 0.392926759236786 76 0.108 0.348500653614355 0.467666666666667 0.745190278576668 0.309898988366062 77 0.132 0.333437617311298 0.374916666666667 0.889364616078144 0.395876149381084 78 0.68 0.323864798132934 0.280416666666667 1.1549413306375 2.099641590936 79 0.117 0.263816850926938 0.246083333333333 1.07206305828759 0.443489487456593 80 0.345 0.212734677132066 0.22325 0.9528988897293 1.62173842389515 81 0.204 0.281360103524922 0.200791666666667 1.40125388765265 0.725049491538625 82 0.227 0.212074419378652 0.191541666666667 1.10719731674737 1.07037897670581 83 0.236 0.280227365473674 0.187833333333333 1.49189369373739 0.842173281688905 84 0.092 0.128932783088253 0.168541666666667 0.76499055478815 0.713550097937677 85 0.138 0.135984141322281 0.158375 0.858621255389303 1.01482421889874 86 0.046 0.129655403957332 0.162166666666667 0.799519448863304 0.35478660045005 87 0.023 0.117961415093308 0.154791666666667 0.762065669512621 0.194979010567201 88 0.009 0.114759302900807 0.154 0.745190278576668 0.078425014552234 89 0.142 0.133515862988731 0.150125 0.889364616078144 1.06354403754994 90 0.207 0.184357509903012 0.159625 1.1549413306375 1.12281837669049 91 0.346 NA NA 1.07206305828759 NA 92 0.207 NA NA 0.9528988897293 NA 93 0.165 NA NA 1.40125388765265 NA 94 0.247 NA NA 1.10719731674737 NA 95 0.123 NA NA 1.49189369373739 NA 96 0.433 NA NA 0.76499055478815 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2011/May/19/t1305831149rbz01ogfyes9to0/1zoe81305831358.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/19/t1305831149rbz01ogfyes9to0/1zoe81305831358.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/19/t1305831149rbz01ogfyes9to0/2u1p61305831358.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/19/t1305831149rbz01ogfyes9to0/2u1p61305831358.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/19/t1305831149rbz01ogfyes9to0/3gpl91305831358.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/19/t1305831149rbz01ogfyes9to0/3gpl91305831358.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/19/t1305831149rbz01ogfyes9to0/40eme1305831358.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/19/t1305831149rbz01ogfyes9to0/40eme1305831358.ps (open in new window)

Parameters (Session):

par1 = Studio 100 PRIJS 2005 ; par2 = Studio 100 PRIJS 2005 ; par3 = Studio 100 PRIJS 2005 ; par4 = 12 ;

Parameters (R input):

par1 = multiplicative ; par2 = 12 ; par3 = Studio 100 PRIJS 2005 ; par4 = 12 ;

R code (references can be found in the software module):

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')

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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