https://github.com/grimmlab/foretis
Time Series Forecasting in Python
Science Score: 23.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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○codemeta.json file
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○.zenodo.json file
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✓DOI references
Found 3 DOI reference(s) in README -
○Academic publication links
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✓Committers with academic emails
1 of 3 committers (33.3%) from academic institutions -
○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (11.1%) to scientific vocabulary
Keywords
Repository
Time Series Forecasting in Python
Basic Info
- Host: GitHub
- Owner: grimmlab
- License: gpl-3.0
- Language: Python
- Default Branch: master
- Homepage: https://foretis.readthedocs.io/en/latest/
- Size: 5.98 MB
Statistics
- Stars: 13
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Topics
Metadata Files
README.md
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.
Owner
- Name: Grimm Lab - Bioinformatics and Machine Learning
- Login: grimmlab
- Kind: organization
- Location: Straubing
- Website: http://www.bit.cs.tum.de
- Repositories: 29
- Profile: https://github.com/grimmlab
Bioinformatics and Machine Learning Lab @ TUM Campus Straubing and HSWT
GitHub Events
Total
- Issues event: 3
- Watch event: 2
- Issue comment event: 5
- Push event: 2
Last Year
- Issues event: 3
- Watch event: 2
- Issue comment event: 5
- Push event: 2
Committers
Last synced: almost 3 years ago
All Time
- Total Commits: 64
- Total Committers: 3
- Avg Commits per committer: 21.333
- Development Distribution Score (DDS): 0.031
Top Committers
| Name | Commits | |
|---|---|---|
| Zepp3 | j****r@t****e | 62 |
| fhaselbeck | 6****k@u****m | 1 |
| fhaselbeck | f****k@h****e | 1 |
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 2
- Total pull requests: 0
- Average time to close issues: about 1 month
- Average time to close pull requests: N/A
- Total issue authors: 1
- Total pull request authors: 0
- Average comments per issue: 3.0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 2
- Pull requests: 0
- Average time to close issues: about 1 month
- Average time to close pull requests: N/A
- Issue authors: 1
- Pull request authors: 0
- Average comments per issue: 3.0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- mojoee (2)
Pull Request Authors
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
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Total downloads:
- pypi 26 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 8
- Total maintainers: 1
pypi.org: foretis
state-of-the-art and easy-to-use time series forecasting
- Homepage: https://github.com/grimmlab/ForeTiS
- Documentation: https://ForeTiS.readthedocs.io/
- License: MIT
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Latest release: 0.0.7
published about 1 year ago