Hi, I am using the aggregate function to generate the hierarchical data (actuals). However, I don't need to aggregate my forecasts since I generate forecasts at each level. The notebook examples use the aggregate function. Does anyone have an example of how my data should look in order to use the HierarchicalLoad function? When I run this, it triple counts everything since I have forecasts at all 3 levels:

Y_fitted_df, S_fitted, tags_fitted = aggregate(Y_fitted_df, hiers)

When I run this, I get an error:

Y_fitted_df, S_fitted, tags_fitted = HierarchicalData.load(Y_fitted2_df)

TypeError: load() missing 1 required positional argument: 'group'

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.