Time series forecasting is fundamental for various use cases in different domains such as energy systems and economics. Creating a forecasting model for a specific use case requires an iterative and complex design process. The typical design process includes five sections (1) data preprocessing, (2) feature engineering, (3) hyperparameter optimization, (4) forecasting method selection, and (5) forecast ensembling, which are commonly organized in a pipeline structure. One promising approach to handle the ever-growing demand for time series forecasts is automating this design process. The article, thus, reviews existing literature on automated time series forecasting pipelines and analyzes how the design process of forecasting models is currently automated. Thereby, we consider both automated machine learning (AutoML) and automated statistical forecasting methods in a single forecasting pipeline. For this purpose, we first present and compare the identified automation methods for each pipeline section. Second, we analyze these automation methods regarding their interaction, combination, and coverage of the five pipeline sections. For both, we discuss the reviewed literature that contributes toward automating the design process, identify problems, give recommendations, and suggest future research. This review reveals that the majority of the reviewed literature only covers two or three of the five pipeline sections. We conclude that future research has to holistically consider the automation of the forecasting pipeline to enable the large-scale application of time series forecasting.

中文翻译：

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审查自动时间序列预测管道

时间序列预测对于能源系统和经济等不同领域的各种用例至关重要。为特定用例创建预测模型需要一个迭代且复杂的设计过程。典型的设计过程包括五个部分 (1) 数据预处理、(2) 特征工程、(3) 超参数优化、(4) 预测方法选择和 (5) 预测集成，它们通常以流水线结构组织。处理对时间序列预测不断增长的需求的一种有前途的方法是自动化这个设计过程。因此，本文回顾了有关自动时间序列预测管道的现有文献，并分析了预测模型的设计过程目前是如何实现自动化的。从而，我们在单个预测管道中同时考虑自动化机器学习 (AutoML) 和自动化统计预测方法。为此，我们首先介绍并比较每个管道部分的已识别自动化方法。其次，我们分析了这些自动化方法的交互、组合和五个管道部分的覆盖范围。对于这两者，我们讨论了有助于自动化设计过程、识别问题、提出建议和建议未来研究的回顾文献。这篇评论表明，大多数评论文献仅涵盖五个管道部分中的两个或三个。我们得出结论，未来的研究必须全面考虑预测管道的自动化，以实现时间序列预测的大规模应用。