HierarchicalForecast: A Python Benchmarking Framework for Hierarchical
Forecasting
- URL: http://arxiv.org/abs/2207.03517v1
- Date: Thu, 7 Jul 2022 18:21:33 GMT
- Title: HierarchicalForecast: A Python Benchmarking Framework for Hierarchical
Forecasting
- Authors: Kin G. Olivares, Federico Garza, David Luo, Cristian Chall\'u and Max
Mergenthaler
- Abstract summary: HierarchicalForecast library contains datasets, evaluation metrics, and a compiled set of statistical baseline models.
Our Python-based framework aims to bridge the gap between statistical, econometric modeling, and Machine Learning forecasting research.
- Score: 0.0
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: Large collections of time series data are commonly organized into
cross-sectional structures with different levels of aggregation; examples
include product and geographical groupings. A necessary condition for coherent
decision-making and planning, with such data sets, is for the dis-aggregated
series' forecasts to add up exactly to the aggregated series forecasts, which
motivates the creation of novel hierarchical forecasting algorithms. The
growing interest of the Machine Learning community in cross-sectional
hierarchical forecasting systems states 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 framework aims to bridge
the gap between statistical, econometric modeling, and Machine Learning
forecasting research. Code and documentation are available in
https://github.com/Nixtla/hierarchicalforecast.
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