Hello,decompose() works with moving averages to define the components of the time series.
stl() uses loess (Local Polynomial Regression Fitting) to define the components.
Regards, Pascal Le 10/08/2012 13:02, Mary Ann Middleton a écrit :
Hi Michael, Thank you so much for your email. That really helped, and with a frequency of 24, I finally have some figures I can work with!! I have a follow up question . Do you have any resources you would recommend that would explain the difference between stl() and decompose()? I have been reading the help files, and googling, but I am not finding the right resources. There are some notable difference between the "random" and the "remainder" in each and I am unsure which is correct for my purposes. At this time, I can use the "random" calculation from decompose() to generate an acf of the data after the seasonal patterns and trends are stripped ( ac f ( n a.omit(x.ts.decomp$random) ) , which is ultimately what I need, however, that isn't solid justification for choosing that calculation. Any pointers appreciated. Cheers, Mary Ann ----- Original Message ----- From: "R. Michael Weylandt" <michael.weyla...@gmail.com> To: "Mary Ann Middleton" <mab...@sfu.ca> Cc: r-help@r-project.org Sent: Thursday, August 9, 2012 2:51:16 PM Subject: Re: [R] POSIXct to ts On Thu, Aug 9, 2012 at 3:30 PM, Mary Ann Middleton <mab...@sfu.ca> wrote:Hi, I have a dataframe (try.1) with date/time and temperature columns, and the date/time is in POSIXct fomat. Sample included below. I would like to to try decompose () or stl() to look at the trends and seasonality in my data, eventually so that I can look at autocorrelation. The series is 3 years of water temperature with clearly visible seasonal periods. Right now, if I try decompose, I get the following error, w hich I beleive is because I haven't correctly defined a time series. "Error in decompose(try.1) : time series has no or less than 2 periods" I am stuck trying to go from POSIXct to as.ts Any suggestions on how to tackle this would be greatly appreciated. Sincerely, Mary Ann A sample of the data looks like this: 'data.frame': 26925 obs. of 2 variables: $ date : POSIXct, format: "2008-07-11 21:00:00" "2008-07-11 22:00:00" ... $ DL_1297699: num 15.3 15.1 14.9 14.6 14.1 ... date DL_1297699 1 2008-07-11 21:00:00 15.318 2 2008-07-11 22:00:00 15.127 3 2008-07-11 23:00:00 14.888 4 2008-07-12 00:00:00 14.553 5 2008-07-12 01:00:00 14.146 6 2008-07-12 02:00:00 13.738 7 2008-07-12 03:00:00 13.401 8 2008-07-12 04:00:00 13.088 9 2008-07-12 05:00:00 12.823 10 2008-07-12 06:00:00 12.630 and the dput(head(x,50) gives this output: structure(list(date = structure(c(1215810000, 1215813600, 1215817200, 1215820800, 1215824400, 1215828000, 1215831600, 1215835200, 1215838800, 1215842400, 1215846000, 1215849600, 1215853200, 1215856800, 1215860400, 1215864000, 1215867600, 1215871200, 1215874800, 1215878400, 1215882000, 1215885600, 1215889200, 1215892800, 1215896400, 1215900000, 1215903600, 1215907200, 1215910800, 1215914400, 1215918000, 1215921600, 1215925200, 1215928800, 1215932400, 1215936000, 1215939600, 1215943200, 1215946800, 1215950400, 1215954000, 1215957600, 1215961200, 1215964800, 1215968400, 1215972000, 1215975600, 1215979200, 1215982800, 1215986400), class = c("POSIXt", "POSIXct"), tzone = "UTC"), DL_1297699 = c(15.318, 15.127, 14.888, 14.553, 14.146, 13.738, 13.401, 13.088, 12.823, 12.63, 12.461, 12.413, 12.461, 12.703, 13.04, 13.497, 14.026, 14.553, 15.031, 15.366, 15.7, 15.819, 15.819, 15.7, 15.605, 15.461, 15.247, 14.984, 14.673, 14.337, 14.002, 13.666, 13.377, 13.137, 12.944, 12.823, 12.847, 13.016, 13.329, 13.762, 14.242, 14.697, 15.175, 15.581, 15.891, 16.034, 16.034, 15.939, 15.772, 15.581)), .Names = c("date", "DL_1297699"), row.names = c(NA, 50L), class = "data.frame")Thank you for the dput()-ed data! The "time series" object that stl() and decompose() expect doesn't have time stamps -- rather it has a "start" and "end" marker as well as a frequency. [For more details, see ?tsp] With your described data, I'd imagine you'd have start = 2008 and frequency = 365*24 (if you have hourly data and an underlying yearly periodicity) but to work with the data you gave, lets suppose 12 hours is a cycle. Note you don't have to give end because that's figured out automatically from frequency and start. x.ts <- ts(x[,2], start = 1, frequency = 12) then I can stl(x, "per") decompose(x) as desired. Hope that helps, Michael [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list 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.
______________________________________________ R-help@r-project.org mailing list 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.
