ForeTiS: A comprehensive time series forecasting framework in Python

Summary: Time series forecasting is a research area with applications in various domains, nevertheless without yielding a predominant method so far. We present ForeTiS, a comprehensive and open source Python framework that allows rigorous training, comparison, and analysis of state-of-the-art time series forecasting approaches. Our framework includes fully automated yet configurable data preprocessing and feature engineering. In addition, we use advanced Bayesian optimization for automatic hyperparameter search. ForeTiS is easy to use, even for non-programmers, requiring only a single line of code to apply state-of-the-art time series forecasting. Various prediction models, ranging from classical forecasting approaches to machine learning techniques and deep learning architectures, are already integrated. More importantly, as a key benefit for researchers aiming to develop new forecasting models, ForeTiS is designed to allow for rapid integration and fair benchmarking in a reliable framework. Thus, we provide a powerful framework for both end users and forecasting experts.

Availability: ForeTiS is available at https://github.com/grimmlab/ForeTiS. We provide a setup using Docker, as well as a Python package at https://pypi.org/project/ForeTiS/. Extensive online documentation with hands-on tutorials and videos can be found at https://foretis.readthedocs.io.

Publikationsart
Zeitschriftenbeiträge (peer-reviewed)
Titel
ForeTiS: A comprehensive time series forecasting framework in Python
Medien
Machine Learning with Applications
Band
12
Artikelnummer
100467
ISBN
2666-8270
Herausgeber
Elsevier
Veröffentlichungsdatum
10.04.2023
Zitation
Eiglsperger, Josef; Haselbeck, Florian; Grimm, Dominik (2023): ForeTiS: A comprehensive time series forecasting framework in Python. Machine Learning with Applications 12, 100467. DOI: 10.1016/j.mlwa.2023.100467