https://github.com/zolabar/trendpy

Time Series Regression with Python
https://github.com/zolabar/trendpy

binder fourier-transform heroku-deployment least-square-regression numpy optimization plotly regression voila-dashboard

Last synced: 7 months ago
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Time Series Regression with Python

• Host: GitHub
• URL: https://github.com/zolabar/trendpy
• Owner: zolabar
• License: mit
• Created: 2022-01-22T16:43:38.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2024-02-21T09:47:33.000Z (over 1 year ago)
• Last Synced: 2025-03-17T15:05:30.370Z (7 months ago)
• Topics: binder, fourier-transform, heroku-deployment, least-square-regression, numpy, optimization, plotly, regression, voila-dashboard
• Language: Jupyter Notebook
• Homepage:
• Size: 13.6 MB
• Stars: 11
• Watchers: 2
• Forks: 3
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Citation: citation.cff

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README

          


[![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/)

# 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 $f(x)=a_n\cdot x^n+a_{n-1}\cdot x^{n-1}+...+a_0$

* 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](https://github.com/RonsenbergVI/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**](https://zolabar.github.io/trendPy/)!

### Voila App



### Streamlit App