similar to: STL - time series seasonal decomposition sensitive to data points?

Hi all, I am trying to analyse a time series data and want to make trend-season decomposition using STL approach in R. However I found the decomposition result seems to be sensitive to data points even with the robust option. More specifically, suppose I have a few years of monthly data. Using stl, I got a decomposition T1 + S1 + R1. Then I deleted the most recent two or three data points, the

G'day, I am looking at monthly reports, and have three series of monthly data from 2007 to 2009. I would like to show the season decomposition of these two series side by side on the one device, however using plot doesn't seem to respect any use of layout(matrix(1:3, ncol=3)) or par(mfcol=c(1,3)). I'm guessing that this means that the plot(stl) perhaps uses them, but I can't find

Hi mailinglist members, I’m actually working on a time series prediction and my current approach is to decompose the series first into a trend, a seasonal component and a remainder. Therefore I’m using the stl() function. But I’m wondering how to get the single components in order to predict the particular fitted series’. This code snippet illustrates my problem: series <-

Hi all, I have a time series that contains double seasonal components (48 and 336) and I would like to decompose the series into the following time series components (trend, seasonal component 1, seasonal component 2 and irregular component). As far as I know, the STL procedure for decomposing a series in R only allows one seasonal component, so I have tried decomposing the series twice. First,

I having issues with interpreting the results of STL decomposition. The following is the data used as well as the decompsed seasonality, trend and the remainder components. It is a weekly data. The original data doesn't appear to be seasonal. But there seems to be a periodic peak in the seasonal component. Can some one please let me know how to interpret the seasonality plot in this??? Also,

Greetings: I'm using the time series decomposition routine "stl" from the package "stats". But how do I get the results into a vector to work with them? example: data(AirPassengers) m<-stl(AirPassengers,"per") print(m) This lists the output but can't figure out how to extract the individual series like seasonal, trend, irregular. Thanks, Bob

Dear all, I have a time series with a frequency of 10 days (so 36 yearly). one year is completely NA. Now I want to do a stl-decomposition, but using e.g. na.action= na.approx makes no sense for a whole year, of course. Is there a way of simulating this single year or to just make stl not using this year for the decomposition? -- View this message in context:

Dear R Helpers, Currently I'm working with the ts package of R and created a TimeSerie from pixels extracted from satellite imagery(S10 NDVI data, 10 daily composites). I'm trying to decompose this signal in different signals (seasonal and trend). When testing out the STL method is says => Only univariate timeseries are allowed, but the current Timeserie I'm using is univariate!

I am trying to understand the nuances of STL (seasonal trend decomposition with loess) based on William Cleveland's (and others?) original development. I do not understand the specification or use of "frequency components" or equivalent "low-pass filter" components in the stl() function. I have run the stl() function on a standard example data (co2) in both S-Plus and

Hi all, I'm working on decomposition and comparison of several time series. I'm interested in extracting the trend components for each time series using the stl function and overlaying them on one another. I'm not sure how to plot the trend function alone and to do the overlay using some kind of loop. If anyone has any insight that would be great! thanks, Katie -- View this

Dear All, Would anyone happen to have tips on how to do a pseudo-additive seasonal decomposition in R? I am working on a ca. 20 year monthly time series on species abundance data, with annual peaks of varying magnitude and zero abundances between the seasonal occurrences. I have tried to use the package "x12", which utilizes x12arima, but without luck so far. More specifically, I am

Hi there, My name is Renan X. Cortes, student of Statistics, from south of Brazil, and I'd like to ask you a few questions about decomposition of time series. In R, when I fit the decomposition using the "stl" function, an object is returned when ask the summary of the fit, called STL.seasonal (%), STL.trend (%) and STL.remainder (%). Once the decomposition is additive,

Hi R-users, I'm trying to produce decompositions of a multiple time-series, grouped by a factor (called "area"). I'm modifying the code in the STLperArea function of package ndvits, as this function only plots produces stl plots, it does not return the underlying data. I want to extract the trend component of each decomposition ("x$time.series[,trend]), assign a name

Hello, Like QR decomposition, I am looking for decomposition to get U and V matrix of SVD decomposition (according to LINPACK, X = UDV'). Do you know if there is a function which could calculate this decomposition? Look forward to your reply, Haleh

Dear all, My data is by minutes and I can see it has seasonal trend by daily and weekly. How do I decompose the minute data into daily and weekly some data: > dput(tail(dt_train,100))structure(c(11L, 11L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 10L,

Hallo you can extract POSIX object tv <- as.POSIXct(index(dt_train)) and use cut together with aggregate cut(tv, "hour") aggregate(dt_train, list(cut(tv, "hour")), mean) 2014-10-06 21:00:00 9.807692 2014-10-06 22:00:00 8.666667 Cheers. Petr ?t 3. 10. 2024 v 17:25 odes?latel roslinazairimah zakaria < roslinaump at gmail.com> napsal: > Dear all, > > My

Hi all, This question is not really R related, rather on Statistics subject itself. Even I did not do those using R. however still I want to post it here, because my hope is I could get help from great statisticians who are the very active member of this group. My problem is to interpret Variance decomposition of VAR model in layman's language. Using EViews I got following : Variance

Hi all, I wonder if this kind of questions are ok in this list... Quick question: What does it mean than the rank of the QR decomposition of a NxN matrix is N-1 ? m: NxN matrix qr(m)$rank equal to (N-1) Long version: I'm doing a manova on a matrix of 10 variables and 16 observations. > dim(tmp) [1] 16 10 > fit <- manova( tmp ~ treatment*mouse ) >results <-

Hi, Would anyone know if it is possible to run a Beveridge Nelson decomposition of a univariate time series object in R? I searched in the help files but didn't come across any potential methods. Thanks very much, Shruthi -- View this message in context: http://www.nabble.com/Beveridge-Nelson-Decomposition-tp21789452p21789452.html Sent from the R help mailing list archive at Nabble.com.

Hi all, I would like to adjust the monthplot() of an stl() so that for a time-series with freq=12 (months): a) the curve on the panel for the k-th month graphs the seasonal values minus their monthly mean values b) add to the fig. the values of the k-th month of the seasonal + remainder, also minus their monthly mean values which corresponds to the 'seasonal diagnostic plot as described by