论文标题
层次FORECAST：Python中层次预测的参考框架
HierarchicalForecast: A Reference Framework for Hierarchical Forecasting in Python
论文作者
论文摘要
大量的时间序列数据通常组织成具有不同聚集水平的结构。示例包括产品和地理组。通常重要的是要确保预测是连贯的，以便将预测值分解级别加起来汇总预测。机器学习社区对层次预测系统的兴趣日益增长，这表明我们正处于一个有利的时刻，以确保科学的努力基于声音基线。因此，我们提出了层次Forecast库，该库包含预处理的公开可用数据集，评估指标和一组编译的统计基线模型。我们基于Python的参考框架旨在弥合统计和计量经济学建模与机器学习预测研究之间的差距。代码和文档可在https://github.com/nixtla/hierarchicalforecast中找到。
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.
