> On Jan 29, 2016, at 12:59 PM, Lorenzo Isella <lorenzo.ise...@gmail.com> wrote: > > Dear All, > I am puzzled and probably I am misunderstanding something. > Please consider the snippet at the end of the email. > We see a time series that has clearly some pattern (essentially, it is > an account where a salary is regularly paid followed by some > expenses). > However the output of the auto.arima from the forecast function does > not seem to make any sense (at least to me). > I wonder if the problem is the fact that the time series is not > defined at regular intervals. > Any suggestions and alternative ways to fit it (e.g.: sarima from the astsa > package to account for the seasonality?) are really welcome. > Many thanks > > Lorenzo > > > > ############################################## > library(forecast) > > tt<-structure(c(1494.5, 1367.57, 1357.57, 1222.23, 1124.02, 1011.64, > 4575.64, 3201.87, 3050.04, 2173.38, 1967.88, 1838.55, 1666.05, > 1656.05, 1524.96, 835.96, 775.36, 592.36, 494.15, 4058.15, 2624.36, > 2448.47, 1598.47, 1398.47, 1264.14, 1165.88, 1053.67, 941.36, > 821.36, 471.36, 373.15, 259.91, 3808.91, 2262.26, 1940.39, 1011.39, > 800.81, 790.81), index = structure(c(16563L, 16565L, 16570L, > 16572L, 16577L, 16579L, 16584L, 16585L, 16586L, 16587L, 16588L, > 16589L, 16590L, 16592L, 16593L, 16599L, 16606L, 16607L, 16608L, > 16612L, 16613L, 16614L, 16617L, 16618L, 16619L, 16620L, 16621L, > 16628L, 16633L, 16635L, 16638L, 16642L, 16647L, 16648L, 16649L, > 16650L, 16651L, 16654L), class = "Date"), class = "zoo") > > plot(tt) >
library(forecast) > fit<-auto.arima(tt) > > ########################################### If , after runing plot(tt), you then run: fitted(fit) Time Series: Start = 16563 End = 16654 Frequency = 1 [1] 1448.8211 NA 1444.8612 NA NA NA NA [8] 1398.7752 NA 1359.0350 NA NA NA NA [15] 1309.1398 NA 1219.7420 NA NA NA NA [22] 2302.8903 3708.1762 2713.0349 2603.0512 1968.0100 1819.1484 1725.4634 [29] NA 1572.6179 1593.2628 NA NA NA NA [36] NA 1258.3403 NA NA NA NA NA [43] NA 1184.9656 955.3023 822.7394 NA NA NA [50] 1987.7634 3333.3131 2294.6941 NA NA 1760.6351 1551.5526 [57] 1406.6751 1309.3682 1238.1899 NA NA NA NA [64] NA NA 1251.6898 NA NA NA NA [71] 1179.9970 NA 988.3885 NA NA 888.4533 NA [78] NA NA 889.4017 NA NA NA NA [85] 1970.0911 3152.7668 2032.3935 1799.2350 1126.2794 NA NA [92] 1088.1525 Using that vector: lines(seq(16563 ,16654 ),fitted(fit), col="red", lwd=3) You can see that the fitted values are capturing quite a bit of the variation. I'm not a regular user of pkg:forecast, so there may be more refined methods of extracting information than using `fitted`. -- David Winsemius Alameda, CA, USA ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.