# HierarchicalForecast: A Reference Framework for Hierarchical Forecasting in Python

arXiv: https://arxiv.org/abs/2207.03517 · HTML: https://mlsanity.com/paper/2207.03517/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 59 references that are in the map.
- First posted on arXiv 7 Jul 2022.

## Builds on (most cited first, 13 of 13)

- [N-BEATS: Neural basis expansion analysis for interpretable time series forecasting](https://mlsanity.com/paper/1905.10437/) (2019)
- [N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting](https://mlsanity.com/paper/2201.12886/) (2022)
- [sktime: A Unified Interface for Machine Learning with Time Series](https://mlsanity.com/paper/1909.07872/) (2019)
- [Darts: User-Friendly Modern Machine Learning for Time Series](https://mlsanity.com/paper/2110.03224/) (2021)
- [Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx](https://mlsanity.com/paper/2104.05522/) (2021)
- [Simultaneously Reconciled Quantile Forecasting of Hierarchically Related Time Series](https://mlsanity.com/paper/2102.12612/) (2021)
- [Probabilistic Hierarchical Forecasting with Deep Poisson Mixtures](https://mlsanity.com/paper/2110.13179/) (2021)
- [Probabilistic forecast reconciliation under the Gaussian framework](https://mlsanity.com/paper/2103.11128/) (2021)
- [Hierarchically Regularized Deep Forecasting](https://mlsanity.com/paper/2106.07630/) (2021)
- [Local scale invariance and robustness of proper scoring rules](https://mlsanity.com/paper/1912.05642/) (2019)
- [FlowDB a large scale precipitation, river, and flash flood dataset](https://mlsanity.com/paper/2012.11154/) (2020)
- [Seglearn: A Python Package for Learning Sequences and Time Series](https://mlsanity.com/paper/1803.08118/) (2018)
- [Orbit: Probabilistic Forecast with Exponential Smoothing](https://mlsanity.com/paper/2004.08492/) (2020)

## Built on by (most cited first, 7 of 7)

- [End-to-End Probabilistic Framework for Learning with Hard Constraints](https://mlsanity.com/paper/2506.07003/) (2025)
- [Hierarchically Coherent Multivariate Mixture Networks](https://mlsanity.com/paper/2305.07089/) (2023)
- [Chronax: A Jax Library for Forecasting and Conformal Inference](https://mlsanity.com/paper/2604.16719/) (2026)
- [Exactness Matters for Physical Rule Enforcement](https://mlsanity.com/paper/2605.08285/) (2026)
- [Hierarchical Spatio-Temporal Transformer for Coherent Emergency Department Forecasting](https://mlsanity.com/paper/2607.27106/) (2026)
- [The Nixtlaverse: An Open-Source Ecosystem for Forecasting](https://mlsanity.com/paper/2609.39741/) (2026)
- [Hierarchical Time Series Forecasting with Operator-Valued Kernels](https://mlsanity.com/paper/2610.18196/) (2026)

## Abstract (arXiv)

Large collections of time series data are commonly organized into structures with different levels of aggregation; examples include product and geographical groupings. It is often important to ensure that the forecasts are coherent so that the predicted values at disaggregate levels add up to the aggregate forecast. The growing interest of the Machine Learning community in hierarchical forecasting systems indicates that we are in a propitious moment to ensure that scientific endeavors are grounded on sound baselines. For this reason, we put forward the HierarchicalForecast library, which contains preprocessed publicly available datasets, evaluation metrics, and a compiled set of statistical baseline models. Our Python-based reference framework aims to bridge the gap between statistical and econometric modeling, and Machine Learning forecasting research. Code and documentation are available in https://github.com/Nixtla/hierarchicalforecast.