Skip to main content

state-of-the-art and easy-to-use time series forecasting

Project description

ForeTiS: A Forecasting Time Series framework

Python 3.8

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.


For more information, installation guides, tutorials and much more, see our documentation:


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


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.

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

ForeTiS-0.0.2.tar.gz (65.8 kB view hashes)

Uploaded Source

Built Distribution

ForeTiS-0.0.2-py3-none-any.whl (91.9 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page