# Prophet: Automatic Forecasting Procedure
[](https://travis-ci.org/facebook/prophet)
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](https://code.facebook.com/projects/) released by Facebook's [Core Data Science team](https://research.fb.com/category/data-science/). It is available for download on [CRAN](https://cran.r-project.org/package=prophet) and [PyPI](https://pypi.python.org/pypi/fbprophet/).
## Important links
- Homepage: https://facebook.github.io/prophet/
- 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
- Prophet R package: https://cran.r-project.org/package=prophet
- Prophet Python package: https://pypi.python.org/pypi/fbprophet/
- Release blogpost: https://research.fb.com/prophet-forecasting-at-scale/
- Prophet paper: Sean J. Taylor, Benjamin Letham (2018) Forecasting at scale. The American Statistician 72(1):37-45 (https://peerj.com/preprints/3190.pdf).
## Installation in R
Prophet is a [CRAN package](https://cran.r-project.org/package=prophet) so you can use `install.packages`:
```
# R
> install.packages('prophet')
```
After installation, you can [get started!](https://facebook.github.io/prophet/docs/quick_start.html#r-api)
### Windows
On Windows, R requires a compiler so you'll need to [follow the instructions](https://github.com/stan-dev/rstan/wiki/Installing-RStan-on-Windows) provided by `rstan`. The key step is installing [Rtools](http://cran.r-project.org/bin/windows/Rtools/) before attempting to install the package.
If you have custom Stan compiler settings, install from source rather than the CRAN binary.
## Installation in Python
Prophet is on PyPI, so you can use pip to install it:
```
# bash
$ pip install fbprophet
```
The major dependency that Prophet has is `pystan`. PyStan has its own [installation instructions](http://pystan.readthedocs.io/en/latest/installation_beginner.html). Install pystan with pip before using pip to install fbprophet.
After installation, you can [get started!](https://facebook.github.io/prophet/docs/quick_start.html#python-api)
If you upgrade the version of PyStan installed on your system, you may need to reinstall fbprophet ([see here](https://github.com/facebook/prophet/issues/324)).
### Windows
On Windows, PyStan requires a compiler so you'll need to [follow the instructions](http://pystan.readthedocs.io/en/latest/windows.html). The key step is installing a recent [C++ compiler](http://landinghub.visualstudio.com/visual-cpp-build-tools).
### Linux
Make sure compilers (gcc, g++, build-essential) and Python development tools (python-dev, python3-dev) are installed. In Red Hat systems, install the packages gcc64 and gcc64-c++. If you are using a VM, be aware that you will need at least 4GB of memory to install fbprophet, and at least 2GB of memory to use fbprophet.
### Anaconda
Use `conda install gcc` to set up gcc. The easiest way to install Prophet is through conda-forge: `conda install -c conda-forge fbprophet`.
## Changelog
### Version 0.3 (2018.06.01)
- Multiplicative seasonality
- Cross validation error metrics and visualizations
- Parameter to set range of potential changepoints
- Unified Stan model for both trend types
- Improved future trend uncertainty for sub-daily data
- Bugfixes
### Version 0.2.1 (2017.11.08)
- Bugfixes
### Version 0.2 (2017.09.02)
- Forecasting with sub-daily data
- Daily seasonality, and custom seasonalities
- Extra regressors
- Access to posterior predictive samples
- Cross-validation function
- Saturating minimums
- Bugfixes
### Version 0.1.1 (2017.04.17)
- Bugfixes
- New options for detecting yearly and weekly seasonality (now the default)
### Version 0.1 (2017.02.23)
- Initial release
github上下载下来的fbprophet
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github上下载下来的fbprophet （307个子文件） 
generated_holidays.csv 2.97MB example_yosemite_temps.csv 451KB example_wp_log_peyton_manning.csv 85KB example_wp_log_R.csv 84KB example_wp_log_R_outliers2.csv 83KB example_wp_log_R_outliers1.csv 78KB data2.csv 21KB data2.csv 21KB data.csv 8KB data.csv 8KB example_retail_sales.csv 5KB example_air_passengers.csv 2KB DESCRIPTION 1KB Gemfile 84B .gitattributes 99B .gitignore 251B .gitignore 73B
katex_render.html 6KB footer.html 3KB slideshow.html 3KB collection_nav.html 2KB post_social_plugins.html 1KB post.html 1KB powered_by.html 1KB nav.html 1KB header_nav.html 1KB social_plugins.html 1022B gridblock.html 966B blog_pagination.html 909B doc.html 852B header.html 831B home_header.html 823B collection_nav_group.html 661B head.html 637B like_button.html 597B collection_nav_group_item.html 491B twitter_share.html 477B twitter_follow.html 477B github_star.html 460B github_watch.html 459B nav_search.html 441B all.html 439B doc_default.html 437B home.html 406B blog_default.html 403B ascii_cinema.html 242B button.html 235B katex_import.html 235B basic.html 217B iframe.html 216B gridblocks.html 212B default.html 209B blog.html 187B doc_page.html 175B index.html 175B top-level.html 167B plain.html 167B all_share.html 166B google_share.html 160B plugin_row.html 160B button.html 128B redirect.html 110B post.html 92B index.html 76B docs.html 48B page.html 21B hero.html 0B doc_paging.html 0B MANIFEST.in 272B non-daily_data.ipynb 1.57MB trend_changepoints.ipynb 1.21MB outliers.ipynb 620KB seasonality,_holiday_effects,_and_regressors.ipynb 590KB quick_start.ipynb 399KB multiplicative_seasonality.ipynb 362KB saturating_forecasts.ipynb 348KB diagnostics.ipynb 269KB uncertainty_intervals.ipynb 102KB LICENSE 18KB LICENSE 1KB LICENSE 1KB LICENSE 75B Gemfile.lock 3KB Makefile 324B Makevars 166B seasonality,_holiday_effects,_and_regressors.md 15KB quick_start.md 9KB diagnostics.md 7KB holiday_effects.md 7KB non-daily_data.md 5KB CONTRIBUTING.md 5KB trend_changepoints.md 5KB uncertainty_intervals.md 4KB README.md 4KB saturating_forecasts.md 3KB multiplicative_seasonality.md 3KB outliers.md 3KB installation.md 2KB contributing.md 2KB index.md 837B共 307 条
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