Welcome

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. Rolling forecast origin and sliding window for time series cross validation

4. Built in daily level datasets

Installation

pip install forecast-tools