# `forecast-tools`: fundamental tools to support the forecasting process in python.
[](https://zenodo.org/badge/latestdoi/250494795)
[](https://orcid.org/0000-0003-2631-4481)
[](https://pypi.python.org/pypi/forecast-tools/)
[](https://opensource.org/licenses/MIT)
[](https://mybinder.org/v2/gh/TomMonks/forecast-tools/master)
[](https://pypi.org/project/forecasta-tools/)
[](https://tommonks.github.io/forecast-tools/)
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. Deliver high 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. Implementation of scale-dependent and relative metrics to evaluate forecast prediction intervals
4. Rolling forecast origin and sliding window for time series cross validation
5. Built in daily level datasets
## 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.
3. Read our [documentation on GitHub pages](https://tommonks.github.io/forecast-tools/)
## 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.3759863
```tex
@software{forecast_tools,
author = {Monks, Thomas},
title = {forecast-tools},
month = dec,
year = 2023,
publisher = {Zenodo},
doi = {10.5281/zenodo.3759863},
url = {https://zenodo.org/doi/10.5281/zenodo.3759863}
}
```
## Contributing to forecast-tools
Please fork Dev, make your modifications, run the unit tests and submit a pull request for review.
> We provide a conda environment for development of forecast-tools. We recommend use of mamba as opposed to conda (although conda will work) as it is FOSS and faster. Install from [mini-forge](https://github.com/conda-forge/miniforge)
Development environment:
```
mamba env create -f binder/environment.yml
mamba activate forecast_dev
```
Unit tests are provided and can be run via `hatch` and its coverage extension. Run the following in the terminal.
To run tests in multiple Python environments (3.8-3.12)
```
hatch test --all
```
To obtain a coverage report run
```
hatch test --cover
```
**All contributions are welcome and must include unit tests!**
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"description": "# `forecast-tools`: fundamental tools to support the forecasting process in python.\n\n[](https://zenodo.org/badge/latestdoi/250494795)\n[](https://orcid.org/0000-0003-2631-4481)\n[](https://pypi.python.org/pypi/forecast-tools/)\n[](https://opensource.org/licenses/MIT)\n[](https://mybinder.org/v2/gh/TomMonks/forecast-tools/master)\n[](https://pypi.org/project/forecasta-tools/)\n[](https://tommonks.github.io/forecast-tools/)\n\n 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.\n\n ## Vision for forecast-tools\n\n 1. Deliver high quality reliable code for forecasting education and practice with full documentation and unit testing.\n 2. Provide a simple to use pythonic interface that users of `statsmodels` and `sklearn` will recognise.\n 3. To improve the quality of Machine Learning time series forecasting and encourage the use of best practice.\n\n## Features:\n\n1. Implementation of classic naive forecast benchmarks such as Naive Forecast 1 along with prediction intervals\n2. Implementation of scale-dependent, relative and scaled forecast errors.\n3. Implementation of scale-dependent and relative metrics to evaluate forecast prediction intervals\n4. Rolling forecast origin and sliding window for time series cross validation\n5. Built in daily level datasets\n\n## Ways to explore forecast-tools\n\n1. `pip install forecast-tools`\n2. Click on the launch-binder at the top of this readme. This will open example Jupyter notebooks in the cloud via Binder.\n3. Read our [documentation on GitHub pages](https://tommonks.github.io/forecast-tools/)\n\n## Citation\n\nIf you use forecast-tools for research, a practical report, education or any reason please include the following citation.\n\n> Monks, Thomas. (2020). forecast-tools: fundamental tools to support the forecasting process in python. Zenodo. http://doi.org/10.5281/zenodo.3759863\n\n```tex\n@software{forecast_tools,\n author = {Monks, Thomas},\n title = {forecast-tools},\n month = dec,\n year = 2023,\n publisher = {Zenodo},\n doi = {10.5281/zenodo.3759863},\n url = {https://zenodo.org/doi/10.5281/zenodo.3759863}\n}\n\n```\n\n## Contributing to forecast-tools\n\nPlease fork Dev, make your modifications, run the unit tests and submit a pull request for review.\n\n> We provide a conda environment for development of forecast-tools. We recommend use of mamba as opposed to conda (although conda will work) as it is FOSS and faster. Install from [mini-forge](https://github.com/conda-forge/miniforge)\n\nDevelopment environment:\n\n```\nmamba env create -f binder/environment.yml\nmamba activate forecast_dev\n```\n\nUnit tests are provided and can be run via `hatch` and its coverage extension. Run the following in the terminal.\n\nTo run tests in multiple Python environments (3.8-3.12)\n\n```\nhatch test --all\n```\n\nTo obtain a coverage report run\n\n```\nhatch test --cover\n```\n\n**All contributions are welcome and must include unit tests!**",
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