[R] Interpolating / smoothing missing time series data
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
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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