Metadata-Version: 2.1
Name: forecast-tools
Version: 0.1.5
Summary: Tools to support the forecasting process in Python
Home-page: https://github.com/TomMonks/forecast-tools
Author: Thomas Monks
Author-email: forecast_tools@gmail.com
License: The MIT License (MIT)
Description: # forecast-tools: fundamental tools to support the forecasting process in python.
        
        [![DOI](https://zenodo.org/badge/250494795.svg)](https://zenodo.org/badge/latestdoi/250494795)
        [![PyPI version fury.io](https://badge.fury.io/py/forecast-tools.svg)](https://pypi.python.org/pypi/forecast-tools/)
        [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
        [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/TomMonks/forecast-tools/master)
        [![Python 3.6+](https://img.shields.io/badge/python-3.6+-blue.svg)](https://www.python.org/downloads/release/python-360+/)
        
         forecast-tools has been developed to support forecasting education and applied forecasting research.  It is MIT licensed and freely available to practitioners, students and researchers via PyPi.  There is a long term plan to make forecast-tools available via conda-forge.
        
         # Vision for forecast-tools
        
         1. Deliverhigh quality reliable code for forecasting education and practice with full documentation and unit testing.
         2. Provide a simple to use pythonic interface that users of `statsmodels` and `sklearn` will recognise.
         3. To improve the quality of Machine Learning time series forecasting and encourage the use of best practice.
        
        # Features:
        
        1. Implementation of classic naive forecast benchmarks such as Naive Forecast 1 along with prediction intervals
        2. Implementation of scale-dependent, relative and scaled forecast errors.
        3. Rolling forecast origin and sliding window for time series cross validation
        4. Built in daily level datasets
        
        ## Two simple ways to explore forecast-tools
        
        1. `pip install forecast-tools`
        2. Click on the launch-binder at the top of this readme. This will open example Jupyter notebooks in the cloud via Binder.
        
        ## Citation
        
        If you use forecast-tools for research, a practical report, education or any reason please include the following citation.
        
        > Monks, Thomas. (2020). forecast-tools: fundamental tools to support the forecasting process in python. Zenodo. http://doi.org/10.5281/zenodo.3969789
        
        ```tex
        @software{forecast_tools_3969789,
          author       = {Thomas Monks},
          title        = {forecast-tools: fundamental tools to support the forecasting process in python},
          year         = 2020,
          publisher    = {Zenodo},
          doi          = {10.5281/zenodo.3969789},
          url          = {https://doi.org/10.5281/zenodo.3969789}
        }
        ```
        
        ## Contributing to forecast-tools
        
        Please fork Dev, make your modifications, run the unit tests and submit a pull request for review.
        
        Development environment:
        
        * `conda env create -f binder/environment.yml`
        
        * `conda activate forecast_dev`
        
        Unit tests are provided and can be run from the command `pytest` and its coverage extension.  Run the following in the terminal.
        
        * `pytest --cov=forecast_tools tests/`
        
        **All contributions are welcome and must include unit tests!**
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6.9
Description-Content-Type: text/markdown
