Include information about model performance in easier to access format

[BryanBaird]

I think this is something that can be calculated from existing data components, but might not currently be packaged up in one consistent format.

Here were some of the analysis questions I encountered that I could not immediately figure out how to address with the current data structure:

• Was the effect of X holiday larger or smaller this year than prior year(s)? By how much? (In absolute count and in relative percent)
• When comparing trends (i.e. across multiple dates), is the gap between forecast and observed growing, shrinking, or staying level?
  • E.g., if a model underestimated holiday impact, do the trends re-converge in the following days or does that gap persist as a step-change?
• Within each country/population, how far off was the forecast value from the observed value on a given date? (In absolute count and relative percent)
• What exactly was the impact of holiday(s) on the forecast value for a given date? (In absolute count and relative percent)
  • This may just be the difference between the detrended forecast and the final forecast.
• What was the estimated impact of holiday(s) on a historical observed value for a given date? (In absolute count and relative percent)
  • This may just be the difference between the detrended amount and the observed amount.

Ideally, this could be included in a simple DataFrame -- either as additional columns in the same general DataFrame output from the general to_df() function, or maybe as a separate dedicated function for performance_df().

(Also acknowledging that this might be different than some other object that stores overall measures of model performance -- things that might come out of Prophet's performance_metrics).