similar to: Exponential smoothing

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 -

Dear all, is there any function to do exponential smoothing of data in R? I have searched all documentation (at least I hope I did) and found nothing. I know exp. smoothing is a somewhat old method and better ones exist, but I want it for the reason of comparing results. Greetings Christoph -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list

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

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?

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

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

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,

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

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

We need help.... We are doing a project for a statistical class in and we are looking at world record times in different running events over time. We are trying to fit the data with a negative exponential but we just cant seem to get a function that works properly. we have on our x-axis the date and on the y-axis the time(in seconds). So as you can imagine, the times have decreased and appear to

If I'm not mistaken, that's sort of local polynomial with even degree and fixed bandwidth (based on my own interpretation of description in Numerical Recipes). You can do that with functions in the KernSmooth package. HTH, Andy > -----Original Message----- > From: wolf at micro-biolytics.com [mailto:wolf at micro-biolytics.com] > Sent: Friday, March 21, 2003 1:43 PM > To:

Hi all, I am attempting to fit, and test the goodness of fit of, a mixed exponential distribution to my dataset which consists of 15minute rainfall intensity data. FYI, the dataset spanning approx.2 years and 7 rainfall stations consists of some three hundred thousand 15min data records, of which some 30 thousand are non-zero rainfall amounts. Could anyone please tell me how i could do

There are a couple of points to keep in mind when doing a log-transform of an exponential model, such as -- y = a*exp(b*x) 1. The implicit statistical model is multiplicative in the error. The implied statistical model of the log transform is -- log(y) = log(a) + b*x + u which implies -- y = a*exp(b*x)*exp(u) A linear regression in the log

Hi there, I cant seem to find a way for the lowess smoothing function to handle "NA" values. Can anyone help?? Regards, Wayne Dr Wayne R. Jones Statistician / Research Analyst KSS Group plc St James''s Buildings 79 Oxford Street Manchester M1 6SS Tel: +44(0)161 609 4084 Mob: +44(0)7810 523 713 KSS Ltd A division of Knowledge Support Systems Group plc Seventh Floor St

Hi all, I have a dataset which consists of 2 columns. I'd like to plot them on a x-y scatter plot and fit an exponential trendline. I'd like R to determine the equation for the trendline and display it on the graph. Since I am new to R (and statistics), any advice on how to achieve this will be greatly appreciated. Many thanks, Chris -- View this message in context:

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 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

Hi, I'm trying to use ad.test or ad2.test to test whether a given data set is exponential. I see that one of the function inputs is "distn", but I try "exponential" and 5 other variants, but I still get the error message: ad.test(test2, distn="exponential", fit=list(estimate = 0.167685), H=NA, sim=100) Error in ad.test(test2, distn = "exponential",

R List, I am seeking help in the area of kernel smoothing. I am attempting to use LSCV values to conduct 2d and weighted 2d kernel smoothing (imported from an ArcMap shapefile), and have been unable to find tools to conduct such analysis in R. My biggest problem is determining the LSCV value and finding a tool that does weighted smoothing, as there are obviously many different packages that will