similar to: Does R support double-exponential smoothing?

I want to forecaste the call number everyday for a call-center. Now I have removed the influence of the fluctuation with some method, so only thing left is to analyze the trend of the call number every day. I have thought of two ways: regression and HoltWinters smooth. But when I use regression, I find some day's call number will bcome negative, which is obviously unreasonabe. If I use

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 would like to create variables in R containing parameters of summary(*Forecast Results*). Using the following code: library(forecast) data <- AirPassengers xets <- ets(data, model="ZZZ", damped=NULL) xfor <- forecast(xets,h=12, level=c(80,95)) summary(xfor) the output is: Forecast method: ETS(M,A,M) Model Information: ETS(M,A,M) Call: ets(y = data, model =

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

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 -

I have been trying to use the ets function in the forecast package on a daily time series (ts2 is a ts object with frequency =7). However when I run the following code I get an error related to etsmodel. I have looked at ets and I can see that there is a call to the function etsmodel, but I cant seem to find info on the ets function anywhere. Does anyone know anything about the etsmodel function?

I am a new R-language user. I have set up a data frame "mydata",one of the colume of which is "skill". Now I want to select the observations whose "skill" value is equal to 1,by what command can I get it?

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 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))

I am a new R user. Now I have some problem while I use R. I have set up a data frame called "mydata". One of the colume of it was "skill". Now I want to select the observations of the frame whose "skill" value is 1,by what command can I get it?

Hi. I have to forecast a time series of a Internet network traffic bitrate. The data are in file http://www.forumaltavilla.it/joomla/datitesi/dati.datand the sampling time is every 0.05 seconds. Now, i want to use HoltWinters forecasting. This is my script. dt=1.58443823e-9 #0.05 seconds in years dati.ts=ts(scan("dati.dat"),start=0,deltat=dt) model=HoltWinters(dati.ts)

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-Users, How can I compute simple moving averages from a time series in R? Note that I do not want to estimate a MA model, just compute the MA's given a lenght (as excel does). Thanks ________________________________________ Ricardo Gonçalves Silva, M. Sc. Apoio aos Processos de Modelagem Matemática Econometria & Inadimplência Serasa S.A. (11) - 6847-8889 ricardosilva@serasa.com.br

Dear list members I want to analyze separately the months of a time series. In other words, I want to plot and fit models for each month separately. Taking the example of http://a-little-book-of-r-for-time-series.readthedocs.org/en/latest/src/timeseries.html births <- scan("http://robjhyndman.com/tsdldata/data/nybirths.dat") birthstimeseries <- ts(births, frequency=12,

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

Hi everyone, I'm using the HoltWinters() function to do a time series analysis. The function only returns the back fitted values ($fitted) after the first year of data, which is my case, is a little more than half. However, when I use the plot() function, it plots the back fit for almost the entire data set. Any ideas on how to extract the fitted values going all the way back to the start

Suppose I have two var x and y,now I want to fits a natural cubic spline in x to y,at the same time create new var containing the smoothed values of y. How can I get it?

Dear all, I stumbled upon what appears to be a troublesome issue when sampling from an ARIMA model (from Rob Hyndman's excellent 'forecast' package) that contains a seasonal AR component. Here's how to reproduce the issue. (I'm using R 2.9.2 with forecast 2.19; see sessionInfo() below). First some data: > x <- c( 0.132475, 0.143119, 0.108104, 0.247291, 0.029510,

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

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