`MinTrace` fails for zero-inflated Time Series with `res_methods` in `['wls_var', 'mint_cov', 'mint_shrink']`

[baniasbaabe]

What happened + What you expected to happen

Applying MinTrace with a res_method will fail due to the following error:

ValueError: Insample residuals close to 0, zero_residual_prc=1.0. Check Y_df``

How can you overcome this? It's probably because some time series has many zeros.

Versions / Dependencies

0.3.0

Reproduction script

# !pip install -U numba statsforecast datasetsforecast
import pandas as pd

# compute base forecast no coherent
from statsforecast.core import StatsForecast
from statsforecast.models import AutoARIMA, Naive

#obtain hierarchical reconciliation methods and evaluation
from hierarchicalforecast.core import HierarchicalReconciliation
from hierarchicalforecast.methods import BottomUp, TopDown, MiddleOut, MinTrace

#obtain hierarchical datasets
from datasetsforecast.hierarchical import HierarchicalData

# Load TourismSmall dataset
Y_df, S, tags = HierarchicalData.load('./data', 'TourismSmall')
Y_df['ds'] = pd.to_datetime(Y_df['ds'])
Y_df['y'] = 0.0


# Compute base level predictions 
sf = StatsForecast(df=Y_df, 
                   models=[AutoARIMA(season_length=12), Naive()], 
                   freq='M', n_jobs=-1)

forecasts_df = sf.forecast(h=12, fitted=True)

# Reconcile the base predictions
reconcilers = [
    MinTrace(method="mint_shrink")
]

hrec = HierarchicalReconciliation(reconcilers=reconcilers)

reconciled_forecasts = hrec.reconcile(Y_df=sf.forecast_fitted_values(), Y_hat_df=forecasts_df, S=S, tags=tags)

Issue Severity

Medium: It is a significant difficulty but I can work around it.

[kdgutier]

Hey @baniasbaabe,

You might want to force it using a bit of Gaussian noise data augmentation.
See the FavoritaHierarchicalDataset.load_item_data example:
https://github.com/Nixtla/hierarchicalforecast/blob/main/experiments/hierarchical_baselines/nbs/run_favorita_baselines.ipynb

We have specialized hierarchical methods on zero inflated processes here:
https://nixtla.github.io/neuralforecast/examples/hierarchicalnetworks.html

The paper of a HINT-related method recently got accepted in the International Journal of Forecasting.
Kin G. Olivares, O. Nganba Meetei, Ruijun Ma, Rohan Reddy, Mengfei Cao, Lee Dicker (2023).”Probabilistic Hierarchical Forecasting with Deep Poisson Mixtures”. International Journal Forecasting, accepted paper. URL https://arxiv.org/pdf/2110.13179.pdf.

[baniasbaabe]

Many thanks for the fast reply, I will look into it!