On 9/7/05 10:19 PM, "Gabor Grothendieck" <[EMAIL PROTECTED]> wrote:
> On 9/7/05, David James <[EMAIL PROTECTED]> wrote: >> The purpose of this email is to ask for pre-built procedures or >> techniques for smoothing and interpolating missing time series data. >> >> I've made some headway on my problem in my spare time. I started >> with an irregular time series with lots of missing data. It even had >> duplicated data. Thanks to zoo, I've cleaned that up -- now I have a >> regular time series with lots of NA's. >> >> I want to use a regression model (i.e. ARIMA) to ill in the gaps. I >> am certainly open to other suggestions, especially if they are easy >> to implement. >> >> My specific questions: >> 1. Presumably, once I get ARIMA working, I still have the problem of >> predicting the past missing values -- I've only seen examples of >> predicting into the future. >> 2. When predicting the past (backcasting), I also want to take >> reasonable steps to make the data look smooth. >> >> I guess I'm looking for a really good example in a textbook or white >> paper (or just an R guru with some experience in this area) that can >> offer some guidance. >> >> Venables and Ripley was a great start (Modern Applied Statistics with >> S). I really had hoped that the "Seasonal ARIMA Models" section on >> page 405 would help. It was helpful, but only to a point. I have a >> hunch (based on me crashing arima numerous times -- maybe I'm just >> new to this and doing things that are unreasonable?) that using >> hourly data just does not mesh well with the seasonal arima code? > > Not sure if this answers your question but if you are looking for something > simple then na.approx in the zoo package will linearly interpolate for you. > >> z <- zoo(c(1,2,NA,4,5)) >> na.approx(z) > 1 2 3 4 5 > 1 2 3 4 5 Alternatively, if you are looking for "more smoothing", you could look at using a moving average or median applied at points of interest with an "appropriate" window size--see wapply in the gplots package (gregmisc bundle). There are a number of other functions that can accomplish the same task. A search for "moving window" or "moving average" in the archives may produce some other ideas. Sean ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html