Efficient Automated Deep Learning for Time Series Forecasting

Deng, Difan; Karl, Florian; Hutter, Frank; Bischl, Bernd und Lindauer, Marius (2023): Efficient Automated Deep Learning for Time Series Forecasting. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), Grenoble, France, 19 - 23 September 2022. In: Machine Learning and Knowledge Discovery in Databases, Lecture Notes in Computer Science Bd. 13715 Cham: Springer. S. 664-680

Abstract

Recent years have witnessed tremendously improved efficiency of Automated Machine Learning (AutoML), especially Automated Deep Learning (AutoDL) systems, but recent work focuses on tabular, image, or NLP tasks. So far, little attention has been paid to general AutoDL frameworks for time series forecasting, despite the enormous success in applying different novel architectures to such tasks. In this paper, we propose an efficient approach for the joint optimization of neural architecture and hyperparameters of the entire data processing pipeline for time series forecasting. In contrast to common NAS search spaces, we designed a novel neural architecture search space covering various state-of-the-art architectures, allowing for an efficient macro-search over different DL approaches. To efficiently search in such a large configuration space, we use Bayesian optimization with multi-fidelity optimization. We empirically study several different budget types enabling efficient multi-fidelity optimization on different forecasting datasets. Furthermore, we compared our resulting system, dubbed Auto-PyTorch-TS, against several established baselines and show that it significantly outperforms all of them across several datasets.

• Dokumententyp: Konferenzbeitrag (Paper)
• Fakultät: Mathematik, Informatik und Statistik > Statistik
• Themengebiete: 000 Informatik, Informationswissenschaft, allgemeine Werke > 004 Informatik ; 500 Naturwissenschaften und Mathematik > 510 Mathematik
• ISBN: 978-3-031-26408-5 ; 978-3-031-26409-2
• ISSN: 0302-9743
• Ort: Cham
• Sprache: Englisch