similar to: HoltWinters fitted level parameter not bounded between 0 (PR#11472)

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

I get John's value (48.8789) in 2.7.0 and R-devel (both on Ubuntu). Really seems to be a numeric issue: > HoltWinters(x, beta = 0, gamma = 0)$alpha alpha 48.87989 > HoltWinters(x * 1.0000000001, beta = 0, gamma = 0)$alpha alpha 0.6881547 > HoltWinters(x * 1.00000000001, beta = 0, gamma = 0)$alpha alpha 48.87989 Providing starting values seems to help, but not

An update on this: I just patched HoltWinters() to use optimize() in the univariate case, and it now computes the correct value. David John Bodley wrote: > Hi, > > Thanks for the quick response. I upgraded by version of R on Windows to the > latest (2.7.0) and re-ran the analysis and get the same result of 48.87989. > > The original time series was a non-regular zoo()

I have seen this question asked and answered many times in a google search but the answers do not always match. I need to configure samba to be able to write but not delete, and to be able to modify and add directories. One person said use file mask of 777 and directory mask of 755. But with this setting they could not add directories. Anouther person said just compile with acl. Is there an easy

I am running R version 2.14.1 with up-to-date packages. When running the HoltWinters function as in HoltWinters(logjj,gamma=FALSE,beta = TRUE) i get back Smoothing parameters: alpha: 0.1692882 beta : TRUE gamma: FALSE In the old days (several weeks ago) i used to get back the actual beta value used as the documentation states. Is this a reporting change? How can I get the value of

Dear All, I was wondering why the forecast for an additive HoltWinters model is given by Yhat[t+h] = a[t] + h * b[t] + s[t + 1 + (h - 1) mod p]. I am a student and new to time series analysis and forecasting. That said, I considered t = 13 and h = 1: Yhat[13+1] = a[13] + b[13] + s[13 + 1] It seems odd that to predict Yhat[14], you would need a s[14] which in turn depends on Y[14], given that

Hi, I am trying to fit the HoltWinters exponential smoothing on a monthly time series data in R. My questions are: 1. I know that the level, trend and seasonality are updated over time. So are the output coefficients a, b and s1-s12 for a specific time t (a,b and s12 for last observataion for example) ? 2. I am trying to work out the fitted values but I could not figure out the h value used in

Hello, I would like to look at the compiled C code behind HoltWinters from the stats package. Is that possible? If so where do I find it? thanks, Spencer [[alternative HTML version deleted]]

every time i try to run HoltWinters i get this error message: > HoltWinters(z, seasonal="additive") Error in decompose(ts(x[1:wind], start = start(x), frequency = f), seasonal) : time series has no or less than 3 periods what's going on? somebody please help me. -- View this message in context:

Hey All, First time on these forums. Thanks in advance. Soooo... I have a process that was functioning well before the 2.14 update. Now the HoltWinters function is throwing an error whereby I get the following: Error in HoltWinters(sales.ts) : optimization failure I've been looking around to determine why this happens (see if I can test the data beforehand) but I haven't come

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

Need your expertise for the theoretical approach to deduce the p-values for the level, trend and seasonality parameters. I wonder if there's source code available. Thanks group. Kel

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)

Dear helpers I'm having a problem with function HoltWinters from package ts. I have a time series that I want to fit an Holt-Winters model and make predictions for the next values. I've already built an object of class ts to serve as input to HoltWinters. But then I get an error; I've used HoltWinters a lot of times and this never hapened > data.HW<-HoltWinters(data.ts) Error

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

Hi, I have a data frame which has a subset of columns containing character representations of dates. The data frame is obtained via the DBI::fetch(rs, ...) method. I would like to convert all the columns which were originally dates in the database to R dates. I can obtain a logical index of columns which are dates via > idx <- dbColumnInfo(rs)$type == "DATE". I'm having

Hello: I have been using R and the locfit package for Unix/Linux for a little while. However, I have had some trouble, as I am trying to do density estimation for bounded independent variables. There is some discussion in the density estimation book by Azzalini, but none in the book by C Loader (creator of locfit). Also, the variables that I am working on are bounded on both sides, not just

Hi, I have a question about CBQ and wonder if I can make a class that are guaranteed 2Mbit but can borrow excess bandwith and only to specific rate. In my exampel below I want to guarantee the network x.x.x.x/y 2Mbit and they can borrow up to MAX 4Mbit. I have tried to create subclasses but it only works like bounded or not. # tc qdisc add dev eth0 root handle 10: cbq bandwidth 10Mbit avpkt

Hi, I'm using aov() to analyze the data and get the rank of factors. However, this does not work for larger set of data due to memory limitation. Are there any similar function to use aov() on data sets larger than memory similar to biglm ? Thanks, ~ Hardi

Can anyone recommend a good nonparametric density approach for data bounded (say between 0 and 1)? For example, using the basic Gaussian density approach doesn't generate a very realistic shape (nor should it): > set.seed(1) > dat <- rbeta(100, 1, 2) > plot(density(dat)) (note the area outside of 0/1) The data I have may be bimodal or have other odd properties (e.g. point mass