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Hi @Ginger Holt; the

HierarchicalLoad

function is meant for benchmarking or research purposes. The idea is to have a function that makes it easier to load famous datasets like tourism or Labour. If you already have forecasts at all different levels, you just need the

S

matrix and the

tags

to use any reconciliation method. The

aggregate

function assumes you just have the lowest-level hierarchy. Currently, we don’t have an explicit function to generate the

S

Matrix or

tags

for the case where the user already has forecasts for every level. However, you can do the following “hack” and it should work fine. 1. Create a

Y_fitted_df_bottom

with just the bottom-level ids. I.e, filter your current

Y_fitted_df

so it has just the ids from the lowest level. 2. Use the aggregate function to generate your

S

Matrix and

tags

. (You can trash the resulting df.) Something like:

Y_fitted_df_trash , S_fitted, tags_fitted = aggregate(Y_fitted_df_bottom, hiers)

3. Use your original

Y_fitted_df,

and the newly generated

S

and

tags

to call the reconciliation method of your choosing.

Hi @Ginger Holt This is an example where I did the hack to recover the

S_df

from a complete hierarchy, you would need to adapt this example to your needs:

# Reading Data
Y_hat_df = pd.read_csv('Y_hat_df.csv')
Y_fitted_df = pd.read_csv('Y_fitted_df.csv')

# Creating bottom df from Y_fitted
unique_ids = Y_hat_df.unique_id.unique()
level_count = np.array([u_id.count('/') for u_id in unique_ids])
is_bottom = (level_count == max(level_count))
bottom_level =  np.array([u_id[4:] for u_id in unique_ids])
bottom_df = pd.DataFrame(dict(unique_id=unique_ids,
                              is_bottom=is_bottom,
                              total_level=['SAU']*len(unique_ids),
                              bottom_level=bottom_level))
bottom_df = bottom_df[bottom_df.is_bottom==True]
bottom_df['ds'] = 1
bottom_df['y'] = 1
bottom_df = bottom_df[['unique_id', 'total_level', 'bottom_level', 'ds', 'y']]

hierarchy_levels = [['total_level'], ['total_level', 'bottom_level']]
_, S_df, tags = aggregate(df=bottom_df, spec=hierarchy_levels)

This is a nice idea for a functionality of the library.