similar to: STL decomposition of time series with multiple seasonalities

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:

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

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,

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

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!

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, I'm trying to transfer an incoming call from the PSTN to another PSTN number through a SER - Asterisk system. SER doing only routing.. pstn call-> SER -> asterisk (call forward) -> SER -> pstn Logic for SER: If something comes from the pstn, send it to asterisk. If something comes from asterisk, send it to the pstn. Every time I am getting a "Got SIP response 481

Hello group, I wanted to try out the asterisk iax protocol between two asterisk machines but have several problems with it. My scenario looks like follows. I am using asterisk 0.9.0 on both machines. SER1 <-> asterisk1 <-> IAX <-> asterisk2 <-> SER2 Both SER and asterisk run on a machine with a public IP address. When the telephone on one side makes a call the telephone

Hi Kevin, no you didn't miss the reply and I've not resolved it yet. Have you got similar problems? Pamela Kevin Fjelsted wrote: >Pamela, >Did you resolve the problems you described? >I didn't see a reply on the list but I may have missed it. > >-Kevin > >-----Original Message----- >From: Pamela Weis [mailto:peawy@gmx.at] >Sent: Thursday, August 05, 2004

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 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

Dear all, I made a simple test of the Cholesky decomposition in the package 'Matrix', by considering 2 variables 100% correlated. http://blogs.sas.com/content/iml/2012/02/08/use-the-cholesky-transformation-to-correlate-and-uncorrelate-variables/ The full code is below and can be simply copy&paste in the R prompt. After uncorrelation I still have a correlation of +-100%...

Dear R-listers, does anybody know of any package developed to implement the Oaxaca-Blinder decomposition in R? I've been googling around and my reserch has been unfruitful. The latest news I've found were 1 year old. Does anybody know of any recent development? Has R ever been employed to run a Oaxaca-Blinder decomposition? Thanks for your kind support, f. [[alternative HTML version

Dear All, I have developed a programme the anable the decomposition of the Gini index, it complets tha valuable work of Achim Zeileis, the author of the ineq package. I would like to make it to be part of all R package. How should I proceed. Must I sent it to the the Core developement team ? The proogramme is written in R. Many thanks for your advice, Best regards, Souleymane

The gchol function in library(kinship) does an LDL decomposition. An updated version has just recently been posted on Rforge, in the bdsmatrix library which is part of survival. > temp <- matrix(c(1,1,1,1,5,8,1,8,14), 3) > gt <- gchol(temp) > as.matrix(gt) # L [,1] [,2] [,3] [1,] 1 0.00 0 [2,] 1 1.00 0 [3,] 1 1.75 1 > diag(gt) # D [1]

Hi wizards, I have seen the function svd of R for singular value decomposition, but I need to computes the ``economy size'' or ``thin'' singular value decomposition of a matrix in R. Somebody knows how to do that?. Thanks in advance. -- Web Page http://geocities.com/lord_tyranus_96/

Can we do Cholesky Decompositon in R for any matrix --------------------------------- [[alternative HTML version deleted]]

I am reading the Mining of Massive Datasets Book by Rajaraman and Ullman. It has a good explanation of Recommendation System at Chapter 9. But what are the relationship between 1) SVD (Singular Decomposition) 2) UV-Decomposition 3) NMF (Non-negative Matrix Factorization) In particular, it seems 2) and 3) can be very similar. Is it right? Thanks. -- View this message in context:

Dear R-list users, this question is not strictly related to R, but hopefully somebody will be able to answer. In a schematic way, which is the algorithm to sample from a multivariate normal distribution using the singular value decomposition? thank you for your help Stefano AVVISO IMPORTANTE: Questo messaggio di posta elettronica pu? contenere informazioni confidenziali, pertanto ? destinato

Dear R list, Please, could someone help me with SSQ decomposition and contrasts. Below my data, graphic, ANOVAs and my doubt: # Data a = paste('a', gl(3, 8), sep='') b = paste('b', gl(2, 4, 24), sep='') tra = sort(paste('t', rep(1:6, 4), sep='')) y = c(26.2, 26.0, 25.0, 25.4, 24.8, 24.6, 26.7, 25.2, 25.7, 26.3, 25.1, 26.4, 19.6,