A library in Python for time series forecasting
Binary Installers: https://pypi.org/project/scikit-forecasts
Source Repository: https://github.com/jdvelasq/scikit-forecasts
Documentation:
scikit-forecast is an open source (distributed under the MIT license) and friendly-user package for time series forecasting. It is considered as a scikit-learn extension specially designed for time series analysis.The package was developed and tested in Python version 3.7. It can be installed from the command line using:
$ pip install scikit-forecasts
Source code can be download from https://github.com/jdvelasq/scikit-forecasts
scikit-forecast can be used interactively at the Python’s command prompt, but a better experience is achieved when IPython or Jupyter’s notebook are used, allows the user to fully document the analysis and plot results. Due to the design of the package, it is easy to use scikit-forecasts with the tools available in the ecosystem of open source tools for data science.
scikit-forecast is well suited for time series analysis and it can be used to:
- Easily include forecasting models
- Easily load time series from a folder
- Time series exploratory analysis
- Time series transformations
- Time series predictions
- Finding the best model for a predicted time series