Ben Letham
2c8419e673
Fix missing m/d on trend component plot. Previously we used MaxNLocator to limit the number of tick marks on the trend and holiday component plots. This was putting the ticks at various points throughout the year, however the tick label showed only the year, which one would incorrectly assume to be Jan 1. This commit removes MaxNLocator and allows matplotlib to set xticks as it pleases, and updates the effected documentation.
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8 年之前 | |
|---|---|---|
| .. | ||
| fbprophet | 8 年之前 | |
| stan | 8 年之前 | |
| LICENSE | 8 年之前 | |
| MANIFEST.in | 8 年之前 | |
| README | 8 年之前 | |
| setup.py | 8 年之前 | |
README
Prophet: Automatic Forecasting Procedure
========================================
Prophet is a procedure for forecasting time series data. It is based on an additive model where non-linear trends are fit with yearly and weekly seasonality, plus holidays. It works best with daily periodicity data with at least one year of historical data. Prophet is robust to missing data, shifts in the trend, and large outliers.
Prophet is `open source software `_ released by Facebook's `Core Data Science team `_.
Full documentation and examples available at the homepage: https://facebookincubator.github.io/prophet/
Important links
---------------
- HTML documentation: https://facebookincubator.github.io/prophet/docs/quick_start.html
- Issue tracker: https://github.com/facebookincubator/prophet/issues
- Source code repository: https://github.com/facebookincubator/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 `_.
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)
========================================
Prophet is a procedure for forecasting time series data. It is based on an additive model where non-linear trends are fit with yearly and weekly seasonality, plus holidays. It works best with daily periodicity data with at least one year of historical data. Prophet is robust to missing data, shifts in the trend, and large outliers.
Prophet is `open source software `_ released by Facebook's `Core Data Science team `_.
Full documentation and examples available at the homepage: https://facebookincubator.github.io/prophet/
Important links
---------------
- HTML documentation: https://facebookincubator.github.io/prophet/docs/quick_start.html
- Issue tracker: https://github.com/facebookincubator/prophet/issues
- Source code repository: https://github.com/facebookincubator/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 `_.
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)