I had to go into the debugger and play with the code myself to figure out what this line was doing 😅 A much simpler way:

intervals_labels = pred_intervals[0].keys()

That may need to be cast to a list for later on, I didn't test fully, but either way it's more readable

Hmm, this doesn't work since pred_intervals is a dict and this would pull the value at key 0 rather than the first value of the dictionary. Is there a better way to pull the first value?

Ah oops, I see what I missed. I don't know of a better way to manipulate the dictionaries, but we could also just do intervals_labels = pd.DataFrame(pred_intervals).index 😂

This is a lot of repeated code with the other logical branch, which is going to make life very hard for us if we ever need to update this code. Could you abstract it out into a local helper function?

Something like

def _get_series_intervals(intervals, residuals, trend_pred_intervals, y):
    return_intervals = {}
    for key, orig_pi_values in intervals.items():
        return_intervals[key] = pd.Series(
            (orig_pi_values.values - residuals.values)
            + trend_pred_intervals[key].values
            + y.values,
            index=origin_pi_values.index
        )
    return return_intervals

if is_multiseries(problem_type):
    for series_id, series_intervals in pred_intervals.items():
        series_id_target_name = self.input_target_name + "_" + str(series_id)
        interval_series_pred_intervals[series_id_target_name] = _get_series_intervals(
            series_intervals,
            residuals[series_id],
            trend_pred_intervals[series_id_target_name],
            y[series_id_target_name]
        )
else:
    trans_pred_intervals = _get_series_intervals(pred_intervals, residuals, trend_pred_intervals, y)

The code I suggested does make a change to the dictionary structure for the multiseries case, which you'll have to let me know if it works or not - I swapped the intervals with series ids, to give us {series_1: {0.75_lower: <>, 0.75_upper: <>, ...}, series_2: {...}...} instead of {0.75_lower: {series_1: <>, series_2: <>, ...}, ...}
Personally, I think this would make it easier to get per-series prediction intervals, but you'll have to let me know if it's too much effort to swap things around at this point. We could also completely overhaul the data structure for this to be something actually 2D like a dataframe instead of nested dictionaries, but that might just be tech debt for the future.

I ended up using your implementation but I tweaked it slightly. I still kept the original dictionary structure since it makes stacking each prediction interval in the end slightly easier. Let me know what you think!

I got so confused here for a second, these names are so similar 😅 can one of them be renamed?

trans_pred_intervals -> transformed_pred_intervals

the word interval has no meaning to me any more 😂

I've read over this like 5 times in a row and I can't figure out what the point of all of it is. It seems to be a lot of rearranging the same data in different ways? I'd love if we can make this clearer, even if it's just through comments.

I gave variable renaming + additional comments adding a shot. Lmk if you think there's a way to additionally clarify it!