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Review of automated time series forecasting pipelines

Meisenbacher, Stefan 1; Turowski, Marian ORCID iD icon 1; Phipps, Kaleb 2; Rätz, Martin; Hagenmeyer, Veit 1; Müller, Dirk; Mikut, Ralf ORCID iD icon 1
1 Institut für Automation und angewandte Informatik (IAI), Karlsruher Institut für Technologie (KIT)
2 Institut für Programmstrukturen und Datenorganisation (IPD), Karlsruher Institut für Technologie (KIT)


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 the five sections (1) data pre-processing, (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 present paper, thus, analyzes the existing literature on automated time series forecasting pipelines to investigate how to automate the design process of forecasting models. Thereby, we consider both Automated Machine Learning (AutoML) and automated statistical forecasting methods in a single forecasting pipeline. For this purpose, we firstly present and compare the proposed automation methods for each pipeline section. Secondly, we analyze the automation methods regarding their interaction, combination, and coverage of the five pipeline sections. ... mehr

Volltext §
DOI: 10.5445/IR/1000147827
Veröffentlicht am 14.06.2022
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Automation und angewandte Informatik (IAI)
Institut für Programmstrukturen und Datenorganisation (IPD)
Publikationstyp Forschungsbericht/Preprint
Publikationsdatum 03.02.2022
Sprache Englisch
Identifikator KITopen-ID: 1000147827
HGF-Programm 37.12.02 (POF IV, LK 01) Design,Operation & Digitalization of the Future Energy Grids
Verlag Karlsruher Institut für Technologie (KIT)
Schlagwörter Automated Machine Learning, Time Series Forecasting; AutoML; Pipeline; Pre-processing, Feature Engineering; Hyperparameter Optimization, Forecasting Method Selection, Ensemble
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