similar to: Exponential smoothing

could someone help me to write a fonction doing an exponential smoothing in case of a multivariate time serie? I tried ewma <- function (x, lambda = 1, init = 0) { if (is.ts(x)) filter(lambda*x, filter=1-lambda, method="recursive", init=init) else stop(message="first argument should be a time serie") } but I can't apply that to multivariate Thanks

Hey, I am using the ets() function in the forecast package to find out the best fit parameters for my time-series. I have about 50 sets of time series data. I'm currently using the function as follows: ets(x,model="AZZ",opt.crit="mse") As to my observation about 5-10 of them have been identified by ets to have a trend and an alpha, beta values have been thrown up -

An r hel search picks up http://tolstoy.newcastle.edu.au/R/help/01a/1162.html from 2001, which points towards the ts package. >>> ?hagen Patrik <Patrik.Ohagen at mpa.se> 19/08/2008 09:00:12 >>> Dear List, I have used all my resources (i.e. "help.search) and I still havn't been able to figure out if there is an Exponential Smoothing command in R. Thank you in

Dear R People: Is there a function for exponential smoothing, please? R version 1.4.1 for Windows. Thanks in advance! Have a great weekend! Sincerely, Erin Hodgess Associate Professor Department of Computer and Mathematical Sciences University of Houston - Downtown 1 Main Street Houston, TX 77002 mailto: hodgess at uhddx01.dt.uh.edu

I want to use double-exponential smoothing to forecast time series datas,but I couldn't find it in the document,does R support this method?

Hello everyone, I was wondering if anyone was aware of a way in which I could use ggplot's stat_smooth() function for add an exponential moving average. I was thinking that I could maybe use something like: >myggplot + stat_smooth (method = 'HoltWinters( data , .9 , 0, 0)') but my efforts were futile. Perhaps there is a way to write my own custom method to throw into

Hello all, I am looking for some help in understanding how to change the way R optimizes the smoothing constant selection process for the HoltWinters function. I'm a SAS veteran but very new to R and still learning my way around. Here is some sample data and the current HoltWinters code I'm using: rawdata <- c(294, 316, 427, 487, 441, 395, 473, 423, 389, 422, 458, 411, 433, 454,

Hi I wonder if you can help me- im trying to do research in R and i cant find anywhere that explains step by step how to do single, double or triple exponential smoothing on a time series dataset. Any help, links or other would be greatly appreciated. [[alternative HTML version deleted]]

Hi R-users, I have a question relating to the HoltWinters() function. I am trying to forecast a series using the Holt Winters methodology but I am getting some unusual results. I had previously been using R for Windows version 2.7.2 and have just started using R 2.9.1. While using version 2.7.2 I was getting reasonable results however upon changing versions I found I started to see unusual

I'm trying to find an R package that will smooth a line with the "polynomial approximation with exponential kernel" or "PAEK" algorithm that is used in ESRI's ArcGIS software. I have a copy of the original paper that provides the algorithm, but I'm not confident in how to code it. The algorithm will smooth a polyline's vertices at a user specified distance

Full_Name: John Bodley Version: 2.5.1 (2007-06-27) OS: Windows XP Submission from: (NULL) (12.144.182.66) I was fitting a number of time series in R using the stats::HoltWinters method to define a single exponential smoothing model, i.e., beta = gamma = 0. I came across an example where the fitted value of alpha was not defined in the [0, 1] interval which seems to violate the lower and upper

Dear R-community, maybe someone can help me with this: I've been using the loess() smoother for quite a while now, and for the matter of documentation I'd like to resolve the acronym LOESS. Unfortunately there's no explanation in the help file, and I didn't get anything convincing from google either. I know that the predecessor LOWESS stands for "Locally Weighted

hello! i don't want to test my sample data for normality, but exponential- lognormal- or gammadistribution. as i've learnt the anderson-darling-test in R is only for normality and i am not supposed to use the kolmogorov-smirnov test of R for parameter estimates from sample data, is that true? can you help me, how to do this anyway! thank you very much! nadja

I have a set of data (in a matrix). I spliced a column out and parsed it as.ts (time series). I then plotted the time series but I found that it was very noisy. I wanted to smooth it out. However, I am having some problems smoothing and plotting the smoothed version. > A <- as.ts(read.table(choose.files())) > x <- as.ts(A[,10]) > plot(x) > > plot(smooth(x))

Dear all, I am pleased to announce the CRAN release of a new package called 'KFAS' - Kalman filter and smoother. The package KFAS contains functions of multivariate Kalman filter, smoother, simulation smoother and forecasting. It uses univariate approach algorithm (aka sequential processing), which is faster than normal method, and it also allows mean square prediction error matrix Ft to

Dear all, I am pleased to announce the CRAN release of a new package called 'KFAS' - Kalman filter and smoother. The package KFAS contains functions of multivariate Kalman filter, smoother, simulation smoother and forecasting. It uses univariate approach algorithm (aka sequential processing), which is faster than normal method, and it also allows mean square prediction error matrix Ft to

Dear R-help, Here's a simple example of nonlinear curve fitting where nls seems to get the answer wrong on a very simple exponential fit (my R version 2.7.2). Look at this code below for a very basic curve fit using nls to fit to (a) a logarithmic and (b) an exponential curve. I did the fits using self-start functions and I compared the results with a more simple fit using a straight lm()

I have some data measured with a coarsely-quantized clock. Let's say the real data are q<- sort(rexp(100,.5)) The quantized form is floor(q), so a simple quantile plot of one against the other can be calculated using: plot(q,type="l"); points(floor(q),col="red") which of course shows the characteristic stair-step. I would like to smooth the quantized

Any one know is there any package or function to generate bivariate exponential distribution? I gusee there should be three parameters, two rate parameters and one correlation parameter. I just did not find any function available on R. Any suggestion is appreciated. -- View this message in context:

Is the matrix exponential available in some package? The cannonical reference is "Nineteen dubious ways to take the exponential of a matrix". (Love that title) Terry T.