Add conformal prediction implementation

[wesstone12]

Replace old prediction interval function create_prediction_intervals() in final_models.R with conformal prediction implementation that uses rolling windows based upon different horizons. Also adds coverage % columns in final outputs for user to add transparency of prediction intervals.

Can view some experiments here: https://github.com/wesstone12/finnts/tree/rollling-window-v3/logs Some basic plots: https://github.com/wesstone12/finnts/tree/rollling-window-v3/plots https://github.com/wesstone12/finnts/tree/rollling-window-v3 also has sandbox.R in main directory to get up and running quick.

Things to look into:

• Calibration split is pretty high due to many users submitting limited data... Would be nice for this to be more dynamic? I'm sure getting good inputs is always a challenge, but not sure how to best go about this
• Rolling windows setup, as there can be many different combinations. Ideally, we could loop over different calibration methodologies to find the best per the data distribution (shouldn't take too long). Would likely take some experimentation with many different datasets
• In future, will need to improve prediction interval construction methodology with more better implementations + as more research comes out
• Need to warn users about how you can't simply just aggregate calibrated outputs (e.g., pivoting monthly data to quarterly), as they will need to re-calibrate
• If coverage is low, prompt user to look more into data inputs
• Plots from https://github.com/wesstone12/finnts/tree/rollling-window-v3/plots, like https://github.com/wesstone12/finnts/blob/rollling-window-v3/plots/final_forecast_M2_arimax--local--R1_meanf--local--R1.png may be interesting to enable a check box if a user wants visualizations in a separate folder

[wesstone12]

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