I am impressed by the R
forecast package, as well as e.g. the
zoo package for irregular time series and interpolation of missing values.
My application is in the area of call center traffic forecasting, so data on weekends is (nearly) always missing, which can be nicely handled by
zoo. Also, some discrete points may be missing, I just use R's
NA for that.
The thing is: all the nice magic of the forecast package, such as
auto.arima() etc, seem to expect plain
ts objects, i.e. equispaced time series not containing any missing data. I think real world applications for equispaced-only time series are definitely existent, but - to my opinion - v e r y limited.
The problem of a few discrete
NA values can easily be solved by using any of the offered interpolation functions in
zoo as well as by
forecast::interp. After that, I run the forecast.
- Does anyone suggest a better solution?
(my main question) At least in my application domain, call center traffic forecasting (and as far as I can imagine most other problem domains), time series are not equispaced. At least we have recurring "business days" scheme or something. What's the best way to handle that and still use all the cool magic of the forecast package?
Should I just "compress" the time series to fill the weekends, do the forecast, and then "inflate" the data again to re-insert NA values in the weekends? (That would be a shame, I think?)
Are there any plans to make the forecast package fully compatible with irregular time series packages like zoo or its? If yes, when and if no, why not?
I'm quite new to forecasting (and statistics in general), so I might overlook something important.