ForeTiS: A Forecasting Time Series framework

ForeTiS is a Python framework that enables the rigorous training, comparison and analysis of time series forecasting for a variety of different models. ForeTiS includes multiple state-of-the-art prediction models or machine learning methods, respectively. These range from classical models, such as regularized linear regression over ensemble learners, e.g. XGBoost, to deep learning-based architectures, such as Multilayer Perceptron (MLP). To enable automatic hyperparameter optimization, we leverage state-of-the-art and efficient Bayesian optimization techniques. In addition, our framework is designed to allow an easy and straightforward integration and benchmarking of further prediction models.

Documentation

For more information, installation guides, tutorials and much more, see our documentation: https://foretis.readthedocs.io/

Contributors

This pipeline is developed and maintained by members of the Bioinformatics lab lead by Prof. Dr. Dominik Grimm:

• Josef Eiglsperger, M.Sc.
• Florian Haselbeck, M.Sc.

Citation

When using ForeTiS, please cite our publication:

ForeTiS: A comprehensive time series forecasting framework in Python.
Josef Eiglsperger*, Florian Haselbeck* and Dominik G. Grimm.
Machine Learning with Applications, 2023. doi: 10.1016/j.mlwa.2023.100467
*These authors have contributed equally to this work and share first authorship.