[R] Interpolating / smoothing missing time series data

David James djames at frontierassoc.com
Thu Sep 8 02:24:00 CEST 2005

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?


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