https://github.com/j-sephb-lt-n/schuberts_leader

A Lightweight Framework for Automatic Discovery of Leading Indicators
https://github.com/j-sephb-lt-n/schuberts_leader

Last synced: about 22 hours ago
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A Lightweight Framework for Automatic Discovery of Leading Indicators

• Host: GitHub
• URL: https://github.com/j-sephb-lt-n/schuberts_leader
• Owner: J-sephB-lt-n
• License: gpl-3.0
• Created: 2022-11-10T08:54:15.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2023-04-03T13:51:01.000Z (over 1 year ago)
• Last Synced: 2024-04-17T22:57:31.683Z (7 months ago)
• Language: Python
• Homepage:
• Size: 5.39 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# schuberts_leader

**A Lightweight Framework for Automatic Discovery of Leading Indicators**

<< THIS PROJECT IS UNDER DEVELOPMENT>>

After cleaning up the code base and creating documentation, I will release it on PyPI

A **leading indicator** is a variable which is predictive of a future outcome. An example is the *number of building permit applications* being a predictor for future demand for housing (i.e. the *number of building permit applications* is a **leading indicator** for *housing demand*). 

[schuberts_leader](https://github.com/J-sephB-lt-n/schuberts_leader) executes repeated random searches through a user-provided list of potential predictive features *X* (at random time lags) in search of features with a leading (possibly non-linear) relationship with a user-provided outcome variable *y*. 

Non-linear relationships are modelled using piecewise-continuous linear [regression splines](https://en.wikipedia.org/wiki/Spline_(mathematics)). 

## Requirements

```

# core requirements:

python:     >=3.7		

numpy:      >=1.2

# additional packages required to run the tutorial examples:

pandas:     >=1.5

matplotlib: >=3.6

```

## Example Usage

TODO