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Time Series Model Hold Out Data and Rerunning the model?

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Time Series Model Hold Out Data and Rerunning the model?

Hello everyone. It is my understanding that if one wants to build multiple time series models on a time series that went from 2000 to today (2015) monthly; and one wanted to use that information to forecast 3 months in the future, it is common approach to split your data into "Train" and "test" datasets.

Your test dataset would be the last 3 months of your time series (jan 2015, feb 2015, march 2015) (pretend we're already in april for simplicity sake). You would then 'define' your model on your Training dataset, and then compute it's errors against your test dataset.

This way you could try out many multiple models and pick the one with the lowest "Forecast prediction error". 

However my question is: By ignorning those last 3 months of data, how do you then use that model to forecast values later in time? Example: say you wanted to forecast April-June. Is it standard procedure to apply the same model (that wasn't built on the last 3 months) to the April - June forecast period?  Or is it standard procedure to 're-build' the model on the entire time series (2000 to 2015 March) and then use that model to forecast into April-June?

I am pretty confused by this and any clarification would be helpful. 

Thanks all!


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‎03-06-2015 10:09 AM
SAS Employee
Posts: 340

Re: Time Series Model Hold Out Data and Rerunning the model?

Posted in reply to Anotherdream

Yes: Standard procedure to re-fit the winning model on the entire time series and then use that model to forecast.

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‎03-06-2015 10:09 AM
SAS Employee
Posts: 340

Re: Time Series Model Hold Out Data and Rerunning the model?

Posted in reply to Anotherdream

Yes: Standard procedure to re-fit the winning model on the entire time series and then use that model to forecast.

SAS Employee
Posts: 416

Re: Time Series Model Hold Out Data and Rerunning the model?

Posted in reply to gergely_batho

Hello -

Attached presentation might be of interest.

Thanks,

Udo

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