{"id":26436173,"url":"https://github.com/zolabar/trendpy","last_synced_at":"2025-03-18T08:15:20.304Z","repository":{"id":39709160,"uuid":"450875943","full_name":"zolabar/trendPy","owner":"zolabar","description":"Time Series Regression with Python","archived":false,"fork":false,"pushed_at":"2024-02-21T09:47:33.000Z","size":14253,"stargazers_count":11,"open_issues_count":2,"forks_count":3,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-03-17T15:05:30.370Z","etag":null,"topics":["binder","fourier-transform","heroku-deployment","least-square-regression","numpy","optimization","plotly","regression","voila-dashboard"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cimg src=\"figures/gallery_logo.PNG\"  height=\"300\"  /\u003e\n\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7009281.svg)](https://doi.org/10.5281/zenodo.7009281) Jupyter Lab:   [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/zolabar/trendPy/HEAD) Documentation: [![doc](https://img.shields.io/badge/Made%20with-Sphinx-1f425f.svg)](https://zolabar.github.io/trendPy/) WebApps: [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/zolabar/trendPy/experimental?urlpath=voila%2Frender%2F/trendpy_webapp.ipynb) (binder) [![Streamlit App](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://zolabar-trendpy-trendpy2-app-kfqshb.streamlit.app/)\n\n# Usage\n\n```pip install trendpy2``` \n\nand 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\n\n* linear $f(x)=a\\cdot x+b$\n* polynomial $f(x)=a_n\\cdot x^n+a_{n-1}\\cdot x^{n-1}+...+a_0$\n* exponential $f(x)=a\\cdot e^{b\\cdot x}$\n* trigonometric $f(x)=a\\cdot \\cos(2\\cdot \\pi\\cdot b\\cdot x+c)$\n* \"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.\n\nin 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!\n\n**trendpy2** is deterministic, i.e. complementary to [trendpy](https://github.com/RonsenbergVI/trendpy), which uses a stochastic approach.\n\nor use one of the **WebApps** with the correspondig button above (voila app or streamlit app).\n\nFor more, have a look at the [**sphinx-documentation**](https://zolabar.github.io/trendPy/)!\n\n### Voila App\n\n\u003cimg src=\"figures/screenshot3.PNG\"  /\u003e\n\n### Streamlit App\n\n\u003cimg src=\"figures/streamlit_app.png\"  /\u003e\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzolabar%2Ftrendpy","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fzolabar%2Ftrendpy","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzolabar%2Ftrendpy/lists"}