Review of automated time series forecasting pipelines

Stefan Meisenbacher, Marian Turowski, Kaleb Phipps, Martin Rätz, Dirk Müller, Veit Hagenmeyer, Ralf Mikut
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
more » ... ndle 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)
doi:10.5445/ir/1000149737 fatcat:k5ugyyx5ujcjjiwfhtcxxtj4iy