Comments (2)
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.
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Many thanks for the fast reply, I will look into it!
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Related Issues (20)
- [Core] Add support for polars
- from hierarchicalforecast.utils import aggregate
- [enhancement] : hierarchicalforecast running so slow on big data HOT 1
- Broken aggregate function in main branch HOT 4
- [Core][Bug] Unbalanced base predictions fail to reshape for`reconciler_args`
- [CORE] Getting 'Categorical categories must be unique' error during the aggregate step HOT 10
- [Utils][Bug] `aggregate` function fails with mixed dtype column. `ValueError: operands could not be broadcast together with shapes` ... HOT 1
- Using Deep Learning Methods HOT 2
- TopDown Approach doesn't raise Exception as expected with `method="forecast_proportions"`
- Test sets includes y as defined
- Exception: min_trace (wls_var) needs covariance matrix to be positive definite. HOT 6
- [utils] aggregate doesn't work correctly with most recent version of the code HOT 1
- Update evaluation example in `README`
- hierarchicalforecast.core - 'list' object has no attribute 'insample' - HOT 2
- Sparse Methods Missing from 0.3.0 HOT 3
- StatsForecast models producing NotImplementedError: tiny datasets in 0.4.0 HOT 5
- MinTraceSparse(nonnegative=True) HOT 1
- [Core][Enhancement]: Add dummies to Aggregate
- Add temporal hierarchies HOT 2
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