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I did as PyStanBackend. And now when we use the method fit of Prophet, we can do like in the documentation: https://facebook.github.io/prophet/docs/additional_topics.html#updating-fitted-models def stan_init(m): """Retrieve parameters from a trained model. Retrieve parameters from a trained model in the format used to initialize a new Stan model. Parameters ---------- m: A trained model of the Prophet class. Returns ------- A Dictionary containing retrieved parameters of m. """ res = {} for pname in ['k', 'm', 'sigma_obs']: res[pname] = m.params[pname][0][0] for pname in ['delta', 'beta']: res[pname] = m.params[pname][0] return res df = pd.read_csv('../examples/example_wp_log_peyton_manning.csv') df1 = df.loc[df['ds'] < '2016-01-19', :] # All data except the last day m1 = Prophet().fit(df1) # A model fit to all data except the last day %timeit m2 = Prophet().fit(df) # Adding the last day, fitting from scratch %timeit m2 = Prophet().fit(df, init=stan_init(m1)) # Adding the last day, warm-starting from m1 Update models.py Update models.py Update models.py Update models.py Update models.py Update models.py Update models.py Test Test2 Test4 Test4 Test are fixed |
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| 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 fbprophet
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/fbprophet and then python -m unittest
Example usage
>>> from fbprophet 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)