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AdrianAntico avatar AdrianAntico commented on May 27, 2024

@mbanco Selecting the confidence levels is a great idea. I think it should be straightforward to add.

What do you mean by adding the forecast function though? Returning the model?

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mbanco avatar mbanco commented on May 27, 2024

Thanks Adrian,
This is the forecast function included in the forecast package:

forecast(
object,
h = ifelse(frequency(object) > 1, 2 * frequency(object), 10),
level = c(80, 95),
xreg = NULL,
...
)

This allows modifying the confidence level or the value of the regressors, etc without having to refit the model.

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AdrianAntico avatar AdrianAntico commented on May 27, 2024

@mbanco Can I just return the model object and the xreg object as well? If you have those two then you can run the forecast function without refitting

I can also have them saved to file which might be the easiest thing to do. That way, you can just load them into R when you need them.

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mbanco avatar mbanco commented on May 27, 2024

If you can return the model object and the xreg object, that would be very good!!

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AdrianAntico avatar AdrianAntico commented on May 27, 2024

@mbanco Let me start by adding a Path argument to the functions so that the model and xregs can be saved to a file location of your choice if a path is supplied. I can do that quickly.

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AdrianAntico avatar AdrianAntico commented on May 27, 2024

@mbanco You can now supply a path to the FilePath arg and the model will be saved and if there are XREG and XREGFC objects, those too (or two) will be saved to file.

Let me know if that works for both requests. With the model output you can reforecast and supply different prediction interval values.

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mbanco avatar mbanco commented on May 27, 2024

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AdrianAntico avatar AdrianAntico commented on May 27, 2024

Let me know how it goes. I think you will need to run RemixAutoML::TimeSeriesDataPrepare() to get the training data to run the forecast() function. I think the PerformanceGrid output from the AutoBandit_() and Auto_() functions will let you know how to set the params in TimeSeriesPrepare(). Let me know if you can't get that to work..

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