0616bfb5da
This commit adds the model_to_dict and model_from_dict functions, using all of the logic that previously lived in model_to_json and model_from_json, and converting those functions to simply reuse the new ones. This is useful because sometimes the user may want to serialize the dict in some other way (e.g. another JSON serialization library such as ujson or orjson, or something entirely different). |
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| prophet | ||
| scripts | ||
| stan | ||
| LICENSE | ||
| MANIFEST.in | ||
| README.md | ||
| requirements.txt | ||
| setup.py | ||
Prophet: Automatic Forecasting Procedure
Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.
Prophet is open source software released by Facebook's Core Data Science team .
Full documentation and examples available at the homepage: https://facebook.github.io/prophet/
Important links
- HTML documentation: https://facebook.github.io/prophet/docs/quick_start.html
- Issue tracker: https://github.com/facebook/prophet/issues
- Source code repository: https://github.com/facebook/prophet
- Implementation of Prophet in R: https://cran.r-project.org/package=prophet
Other forecasting packages
- Rob Hyndman's forecast package
- Statsmodels
Installation
pip install prophet
Note: Installation requires PyStan, which has its own installation instructions. On Windows, PyStan requires a compiler so you'll need to follow the instructions. The key step is installing a recent C++ compiler
Installation using Docker and docker-compose (via Makefile)
Simply type make build and if everything is fine you should be able to make shell or alternative jump directly to make py-shell.
To run the tests, inside the container cd python/prophet and then python -m unittest
Example usage
>>> from prophet import Prophet
>>> m = Prophet()
>>> m.fit(df) # df is a pandas.DataFrame with 'y' and 'ds' columns
>>> future = m.make_future_dataframe(periods=365)
>>> m.predict(future)