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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2207.03517 |
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| _version_ | 1866913539523870720 |
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| author | Olivares, Kin G. Garza, Azul Luo, David Challú, Cristian Mergenthaler, Max Taieb, Souhaib Ben Wickramasuriya, Shanika L. Dubrawski, Artur |
| author_facet | Olivares, Kin G. Garza, Azul Luo, David Challú, Cristian Mergenthaler, Max Taieb, Souhaib Ben Wickramasuriya, Shanika L. Dubrawski, Artur |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2207_03517 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | HierarchicalForecast: A Reference Framework for Hierarchical Forecasting in Python Olivares, Kin G. Garza, Azul Luo, David Challú, Cristian Mergenthaler, Max Taieb, Souhaib Ben Wickramasuriya, Shanika L. Dubrawski, Artur Machine Learning Artificial Intelligence 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. |
| title | HierarchicalForecast: A Reference Framework for Hierarchical Forecasting in Python |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2207.03517 |