Science Score: 67.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 3 DOI reference(s) in README -
✓Academic publication links
Links to: zenodo.org -
○Committers with academic emails
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (7.3%) to scientific vocabulary
Keywords
Repository
Time Series Regression with Python
Basic Info
Statistics
- Stars: 11
- Watchers: 2
- Forks: 3
- Open Issues: 2
- Releases: 2
Topics
Metadata Files
README.md
Jupyter Lab: 
Documentation: 
WebApps: (binder) 
Usage
pip install trendpy2
and use it as trendpy2 as shown in the example.ipynb and approximate your time series ($f:\mathbb{R}\to \mathbb{R}$) with the following trends
- linear $f(x)=a\cdot x+b$
- polynomial $f(x)=an\cdot x^n+a{n-1}\cdot x^{n-1}+...+a_0$
- exponential $f(x)=a\cdot e^{b\cdot x}$
- trigonometric $f(x)=a\cdot \cos(2\cdot \pi\cdot b\cdot x+c)$
- "free" (for max. three parameters) (e.g.
a*arctan(b*x+c),a*exp(b*x+c),a*(x*b)+c), the intial guess for a, b, c is 1.
in your Python scripts or jupyter notebooks and use the best of the numerical and symbolic worlds to make predictions and assess the quality of your fit!
trendpy2 is deterministic, i.e. complementary to trendpy, which uses a stochastic approach.
or use one of the WebApps with the correspondig button above (voila app or streamlit app).
For more, have a look at the sphinx-documentation!
Voila App
Streamlit App
Owner
- Name: Zoufiné Lauer-Baré
- Login: zolabar
- Kind: user
- Website: https://zolabar.github.io/
- Twitter: zoulabar
- Repositories: 5
- Profile: https://github.com/zolabar
Citation (citation.cff)
cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: Lauer-Bare
given-names: Zoufine
orcid: https://orcid.org/0000-0002-7083-6909
- family-names: Wirth
given-names: Marcus
title: trendPy - Time Series Regression with Python
version: v1.0.1
journal: Zenodo
doi: 10.5281/zenodo.7009281
date-released: 2022-07-15
url: https://github.com/zolabar/trendPy
GitHub Events
Total
Last Year
Committers
Last synced: about 2 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| Zoufiné Lauer-Baré | 8****r | 89 |
| zolabar | r****e@g****m | 25 |
| Marcus02W | 1****W | 6 |
| hadamard-zweistein | z****e@g****m | 1 |
| S212376 | 1****6 | 1 |
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 11
- Total pull requests: 11
- Average time to close issues: 19 days
- Average time to close pull requests: 23 minutes
- Total issue authors: 2
- Total pull request authors: 3
- Average comments per issue: 0.82
- Average comments per pull request: 0.0
- Merged pull requests: 11
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- zolabar (6)
- Marcus02W (2)
Pull Request Authors
- zolabar (5)
- Marcus02W (5)
- Joschua-J (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
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Total downloads:
- pypi 8 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 1
- Total maintainers: 1
pypi.org: trendpy2
Time series regression with python
- Homepage: https://github.com/zolabar/trendPy/tree/main
- Documentation: https://trendpy2.readthedocs.io/
- License: MIT License
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Latest release: 1.0.2
published about 2 years ago
Rankings
Maintainers (1)
Dependencies
- jupyter *
- matplotlib *
- numpy *
- pandas *
- plotly *
- scipy *
- sympy *
- voila *