similar to: Seasonality - Centered MA vs. Holt-Winters

Hi, I'd like to know if the Holt-Winters function in R can modify the alpha paremeter in an "intelligent" way. I know it can vary from 0 to 1. Wich mathematical formula does it use to calculate the correct value of alpha? Thanks, bye. Carlo

Dear Members, I have been searching for a package in R which can handle multiple seasonality suggested by taylor(2003). It will be great help if anybody has used this on R before (i.e. which package). Thanks in Advance. Best Regards Atul Malik [[alternative HTML version deleted]]

I'm trying to find a good open source software to do sales forecasting using Holt Winters and Box Jenkins time series algorithm. Somebody pointed me that R is the best open source available for statistical computing. Are there functions to do Holt Winters and Box Jenkins time series prediction in R? If there is none, can some one point me a good GNU/freeware to do the sales forecasting using

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

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

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!

1. This is an imaginary data on monthly outcomes of 2 years and I want to forecast the outcome for next 12 months of next year. data Data1; input Yr Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec; datalines; 2008 12 13 12 14 13 12 11 15 10 12 12 12 2009 12 13 12 14 13 12 11 15 10 12 12 12 ; run; I converted the above data into the below format to use it in R as it was giving error: asking

Hi, I'm new here. I need to use R to analyze a particular time serie. I have to estimate the pubblication of a news of an online newspaper, for example in the CNN site. I have many text files and every file correspond to a day. In every file I have two columns: 1) in the first column there is the pubblication time of the news 2) in the second column there is the distance, expressed in

Hello R People: When using the arima function with the seasonal option, are the seasonal options only good for monthly and quarterly data, please? Also, I believe that weekly and daily data are not appropriate for seasonal parm estimation via arima. Is that correct, please? Thanks, Sincerely, Laura Holt mailto: lauraholt_983 at hotmail.com download!

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()

Dear R community, I have a project with electricity load forecasting, and I got hourly data for system load. If you haven't worked with electricity before, seasonality comes in many flavors: a daily pattern, with a peak at around 7pm; a weekly pattern, in which we use more electricity on weekdays in comparison to weekends; a winter-summer pattern, with air conditioning and heaters playing an

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,

A rather strange situation here and I've not found the source of the problem. The point is to print a ternary plot matrix of compositional data with ellipses enclosing 95% of the variance in each plot. The ellipses display on the monitor, dev = x11cairo (see attached winters-x11cairo.pdf), but not when sent directly to a file, dev = pdf (see attached winters-pdf.pdf). Here's

I have read ?zoo but am not sure how to relate the parameters (x, order.by, frequency, and style) to my data.frame. The structure of the data.frame is 'data.frame': 11169 obs. of 4 variables: $ stream : Factor w/ 37 levels "Burns","CIL",..: 1 1 1 1 1 1 1 1 1 1 ... $ sampdate: Date, format: "1987-07-23" "1987-09-17" ... $ param : Factor w/

please i've a big problem. i've to do a econometric-quantitative methods assignment about the canadian lynx, the problem is that i really i don't know how to use r and how to apply all the steps. I begun the time plot, ACF and PACF but i'm not able to decide what is the correct model of ARIMA, Holt-winter, ecc to forecast the next 20 years of canadian lynx's cyle... if someone

I have posted my problem on the link below, but didn't get any answer. I am hoping someone here can help me with this issue. Here's my problem: I am using H323 to talk between Asterisk and Avaya IP Office 500. For some strange reason, when we are talking on a VoIP call, we get disconnected after 10+ minutes. We have two other Elastix box, but none of them are getting disconnected. From

I'm uncertain if this is perhaps a stupid question: I want to create "centered" dummy variables to use in a call to glm(), and wondering if there's some slick method in R to do so. That is, rather than have a factor, which results in a glm() fit returning coefficients specifying either absence or presence of the factor, I'd like to fit a glm() without intercept such that

Working with 5 subset streams from my source data frame, three of them successfully call dcast(), but two fail: jerritt.cast <- dcast(jerritt.melt, site + sampdate ~ param) Aggregation function missing: defaulting to length and winters.cast <- dcast(winters.melt, site + sampdate ~ param) Aggregation function missing: defaulting to length Yet both data frames have the values in their

Here is a patch for place plugin. It add a "place_mode" option wich actually can have two values: "Cascade" (original placement mode) and "Centered". Thanks for any comment! Cedric -------------- next part -------------- A non-text attachment was scrubbed... Name: place.patch Type: text/x-diff Size: 3556 bytes Desc: not available Url :

I tried make a pca analysis with R. For this propose i need centered rows and columns. I make this using the function princomp: > zz<-princomp(x,cor=TRUE) When i do it each column have mean=0 and standard deviation=1. But i have this problem: the total inertia, sum of Eigenval., dont is = =number of columns * 1= sum of total standard deviation Why? It's a bug in