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Dismiss alert {{ message }} facebook / prophet Public * Notifications * Fork 4.4k * Star 16.6k Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth. facebook.github.io/prophet License MIT license 16.6k stars 4.4k forks Activity Star Notifications * Code * Issues 372 * Pull requests 4 * Actions * Projects 0 * Security * Insights More * Code * Issues * Pull requests * Actions * Projects * Security * Insights facebook/prophet This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. main Switch branches/tags [ ] Branches Tags Could not load branches Nothing to show {{ refName }} default View all branches Could not load tags Nothing to show {{ refName }} default View all tags Name already in use A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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Latest commit @dchiang00 dchiang00 Add predict_columns parameter to cross validation (#2486) ... 53b9b1a Sep 20, 2023 Add predict_columns parameter to cross validation (#2486) 53b9b1a Git stats * 779 commits Files Permalink Failed to load latest commit information. Type Name Latest commit message Commit time .github/workflows Update check config for R Github Actions (#2439) May 28, 2023 17:55 R Bump version numbers for release (#2441) May 30, 2023 00:26 docs Updated documentation and README (#2427) May 28, 2023 23:31 examples Add example dataset affected by COVID and guide for docs (#2260) September 6, 2022 03:29 notebooks Sanitize warm start parameters (#2342) January 15, 2023 00:05 python Add predict_columns parameter to cross validation (#2486) September 20, 2023 11:56 python_shim Update python-holidays integration (#2379) May 28, 2023 17:31 .deepsource.toml Pakage rename (#1844) March 21, 2021 14:13 .gitattributes Fix repo language details February 27, 2017 14:37 .gitignore gitignore more dev files July 3, 2023 21:09 .travis.yml Pakage rename (#1844) March 21, 2021 14:13 CODE_OF_CONDUCT.md OSS Automated Fix: Addition of Code of Conduct March 27, 2019 15:26 Dockerfile Simplify setup.py and tests (#2336) January 19, 2023 21:52 LICENSE Change to MIT license May 21, 2019 11:40 Makefile Remove test command in make file since test are run from insede conta... May 31, 2019 11:23 README.md Correct R install command in README.md (#2445) May 31, 2023 14:43 docker-compose.yml reformat code May 31, 2019 11:23 View code [ ] Prophet: Automatic Forecasting Procedure Important links Installation in R - CRAN Installation in R - Latest release Experimental backend - cmdstanr Windows Installation in Python - PyPI release Anaconda Installation in Python - Development version Linux Windows Changelog Version 1.1.4 (2023.05.30) Python R Version 1.1.2 (2023.01.20) Python R Version 1.1.1 (2022.09.08) Version 1.1 (2022.06.25) Version 1.0 (2021.03.28) Version 0.7 (2020.09.05) Version 0.6 (2020.03.03) Version 0.5 (2019.05.14) Version 0.4 (2018.12.18) Version 0.3 (2018.06.01) Version 0.2.1 (2017.11.08) Version 0.2 (2017.09.02) Version 0.1.1 (2017.04.17) Version 0.1 (2017.02.23) License README.md Prophet: Automatic Forecasting Procedure Build PyPI Version PyPI Downloads Monthly PyPI Downloads All CRAN Version CRAN Downloads Monthly CRAN Downloads All Conda_Version --------------------------------------------------------------------- 2023 Update: We discuss our plans for the future of Prophet in this blog post: facebook/prophet in 2023 and beyond --------------------------------------------------------------------- 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. It is available for download on CRAN and PyPI. 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 * Contributing: https://facebook.github.io/prophet/docs/ contributing.html * Prophet R package: https://cran.r-project.org/package=prophet * Prophet Python package: https://pypi.python.org/pypi/prophet/ * Release blogpost: https://research.facebook.com/blog/2017/2/ 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 - CRAN [?][?] The CRAN version of prophet is fairly outdated. To get the latest bug fixes and updated country holiday data, we suggest installing the latest release. Prophet is a CRAN package so you can use install.packages. install.packages('prophet') After installation, you can get started! Installation in R - Latest release install.packages('remotes') remotes::install_github('facebook/prophet@*release', subdir = 'R') Experimental backend - cmdstanr You can also choose an experimental alternative stan backend called cmdstanr. Once you've installed prophet, follow these instructions to use cmdstanr instead of rstan as the backend: # R # We recommend running this in a fresh R session or restarting your current session install.packages(c("cmdstanr", "posterior"), repos = c("https://mc-stan.org/r-packages/", getOption("repos"))) # If you haven't installed cmdstan before, run: cmdstanr::install_cmdstan() # Otherwise, you can point cmdstanr to your cmdstan path: cmdstanr::set_cmdstan_path(path = ) # Set the R_STAN_BACKEND environment variable Sys.setenv(R_STAN_BACKEND = "CMDSTANR") Windows On Windows, R requires a compiler so you'll need to follow the instructions provided by rstan. The key step is installing 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 - PyPI release Prophet is on PyPI, so you can use pip to install it. python -m pip install prophet * From v0.6 onwards, Python 2 is no longer supported. * As of v1.0, the package name on PyPI is "prophet"; prior to v1.0 it was "fbprophet". * As of v1.1, the minimum supported Python version is 3.7. After installation, you can get started! Anaconda Prophet can also be installed through conda-forge. conda install -c conda-forge prophet Installation in Python - Development version To get the latest code changes as they are merged, you can clone this repo and build from source manually. This is not guaranteed to be stable. git clone https://github.com/facebook/prophet.git cd prophet/python python -m pip install -e . By default, Prophet will use a fixed version of cmdstan (downloading and installing it if necessary) to compile the model executables. If this is undesired and you would like to use your own existing cmdstan installation, you can set the environment variable PROPHET_REPACKAGE_CMDSTAN to False: export PROPHET_REPACKAGE_CMDSTAN=False; python -m pip install -e . 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 prophet, and at least 2GB of memory to use prophet. Windows Using cmdstanpy with Windows requires a Unix-compatible C compiler such as mingw-gcc. If cmdstanpy is installed first, one can be installed via the cmdstanpy.install_cxx_toolchain command. Changelog Version 1.1.4 (2023.05.30) Python * We now rely solely on holidays package for country holidays. * Upgraded cmdstan version to 2.31.0, enabling Apple M1 support. * Fixed bug with Windows installation caused by long paths. R * Updated holidays data based on holidays version 0.25. Version 1.1.2 (2023.01.20) Python * Sped up .predict() by up to 10x by removing intermediate DataFrame creations. * Sped up fourier series generation, leading to at least 1.5x speed improvement for train() and predict() pipelines. * Fixed bug in how warm start values were being read. * Wheels are now version-agnostic. R * Fixed a bug in construct_holiday_dataframe() * Updated holidays data based on holidays version 0.18. Version 1.1.1 (2022.09.08) * (Python) Improved runtime (3-7x) of uncertainty predictions via vectorization. * Bugfixes relating to Python package versions and R holiday objects. Version 1.1 (2022.06.25) * Replaced pystan2 dependency with cmdstan + cmdstanpy. * Pre-packaged model binaries for Python package, uploaded binary distributions to PyPI. * Improvements in the stan model code, cross-validation metric calculations, holidays. Version 1.0 (2021.03.28) * Python package name changed from fbprophet to prophet * Fixed R Windows build issues to get latest version back on CRAN * Improvements in serialization, holidays, and R timezone handling * Plotting improvements Version 0.7 (2020.09.05) * Built-in json serialization * Added "flat" growth option * Bugfixes related to holidays and pandas * Plotting improvements * Improvements in cross validation, such as parallelization and directly specifying cutoffs Version 0.6 (2020.03.03) * Fix bugs related to upstream changes in holidays and pandas packages. * Compile model during first use, not during install (to comply with CRAN policy) * cmdstanpy backend now available in Python * Python 2 no longer supported Version 0.5 (2019.05.14) * Conditional seasonalities * Improved cross validation estimates * Plotly plot in Python * Bugfixes Version 0.4 (2018.12.18) * Added holidays functionality * Bugfixes 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 License Prophet is licensed under the MIT license. About Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth. facebook.github.io/prophet Topics python r forecasting Resources Readme License MIT license Code of conduct Code of conduct Security policy Security policy Activity Stars 16.6k stars Watchers 427 watching Forks 4.4k forks Report repository Releases 15 1.1.4 Latest May 29, 2023 + 14 releases Packages 0 No packages published Used by 11 * @027xiguapi * @TotallyMaliciousCryptoBro * @aprilaili * @llin590 * @dr-bestore * @masfahru + 3 Contributors 157 * @bletham * @tcuongd * @seanjtaylor * @ryankarlos * @dependabot[bot] * @baogorek * @seriousran * @Igevorse * @joseangel-sc * @sss-ng * @lemonlaug + 146 contributors Languages * Python 59.8% * R 38.4% * Stan 1.8% Footer (c) 2023 GitHub, Inc. Footer navigation * Terms * Privacy * Security * Status * Docs * Contact GitHub * Pricing * API * Training * Blog * About You can't perform that action at this time.