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. It is available for download on CRAN and PyPI.
Prophet alternatives and similar packages
Based on the "Machine Learning" category.
Alternatively, view Prophet alternatives based on common mentions on social networks and blogs.
10.0 10.0 L1 Prophet VS tensorflowAn Open Source Machine Learning Framework for Everyone
9.8 9.5 Prophet VS gymA toolkit for developing and comparing reinforcement learning algorithms.
9.8 9.6 L1 Prophet VS xgboostScalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
9.6 0.0 L1 Prophet VS CNTKMicrosoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit
9.6 10.0 L1 Prophet VS PaddlePaddlePArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）
9.3 0.0 L3 Prophet VS TFLearnDeep learning library featuring a higher-level API for TensorFlow.
8.9 0.0 L3 Prophet VS NuPICNumenta Platform for Intelligent Computing is an implementation of Hierarchical Temporal Memory (HTM), a theory of intelligence based strictly on the neuroscience of the neocortex.
8.9 9.8 Prophet VS H2OH2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
8.0 0.6 L2 Prophet VS Pylearn2Warning: This project does not have any current developer. See bellow.
7.9 6.4 L4 Prophet VS LightFMA Python implementation of LightFM, a hybrid recommendation algorithm.
7.7 4.3 Prophet VS SacredSacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.
7.6 1.3 L4 Prophet VS skflowSimplified interface for TensorFlow (mimicking Scikit Learn) for Deep Learning
5.8 0.0 L2 Prophet VS CrabCrab is a ﬂexible, fast recommender engine for Python that integrates classic information ﬁltering recommendation algorithms in the world of scientiﬁc Python packages (numpy, scipy, matplotlib).
4.3 0.0 Prophet VS seqevalA Python framework for sequence labeling evaluation(named-entity recognition, pos tagging, etc...)
An implementation of the TrueSkill rating system for Python
4.0 7.5 Prophet VS adaptive:chart_with_upwards_trend: Adaptive: parallel active learning of mathematical functions
SciKit-Learn Laboratory (SKLL) makes it easy to run machine learning experiments.
3.5 0.0 L4 Prophet VS Feature ForgeA set of tools for creating and testing machine learning features, with a scikit-learn compatible API
The easiest way to use Machine Learning. Mix and match underlying ML libraries and data set sources. Generate new datasets or modify existing ones with ease.
2.9 9.6 Prophet VS bodyworkML pipeline orchestration and model deployments on Kubernetes, made really easy.
2.1 8.1 Prophet VS OptaPyOptaPy is an AI constraint solver for Python to optimize planning and scheduling problems.
1.9 8.8 Prophet VS openskill.pyA faster, open-license alternative to Microsoft TrueSkill
1.3 3.3 Prophet VS neptune-contribThis library is a location of the LegacyLogger for PyTorch Lightning.
* Code Quality Rankings and insights are calculated and provided by Lumnify.
They vary from L1 to L5 with "L5" being the highest.
Do you think we are missing an alternative of Prophet or a related project?
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.
- 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.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 so you can use
After installation, you can get started!
Experimental backend - cmdstanr
You can also choose an experimental alternative stan backend called
cmdstanr. Once you've installed
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 = <your existing cmdstan>) # Set the R_STAN_BACKEND environment variable Sys.setenv(R_STAN_BACKEND = "CMDSTANR")
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. 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".
# Install pystan with pip before using pip to install prophet # pystan>=3.0 is currently not supported pip install pystan==126.96.36.199 pip install prophet
The default dependency that Prophet has is
pystan. PyStan has its own installation instructions. Install pystan with pip before using pip to install prophet.
Experimental backend - cmdstanpy
You can also choose a (more experimental) alternative stan backend called
cmdstanpy. It requires the CmdStan command line interface and you will have to specify the environment variable
STAN_BACKEND pointing to it, for example:
# bash $ CMDSTAN=/tmp/cmdstan-2.22.1 STAN_BACKEND=CMDSTANPY pip install prophet
Note that the
CMDSTAN variable is directly related to
cmdstanpy module and can be omitted if your CmdStan binaries are in your
It is also possible to install Prophet with two backends:
# bash $ CMDSTAN=/tmp/cmdstan-2.22.1 STAN_BACKEND=PYSTAN,CMDSTANPY pip install prophet
After installation, you can get started!
If you upgrade the version of PyStan installed on your system, you may need to reinstall prophet (see here).
conda install gcc to set up gcc. The easiest way to install Prophet is through conda-forge:
conda install -c conda-forge prophet.
On Windows, PyStan requires a compiler so you'll need to follow the instructions. The easiest way to install Prophet in Windows is in Anaconda.
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.
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
- 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
- Compile model during first use, not during install (to comply with CRAN policy)
cmdstanpybackend 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
Version 0.4 (2018.12.18)
- Added holidays functionality
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
Version 0.2.1 (2017.11.08)
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
Version 0.1.1 (2017.04.17)
- New options for detecting yearly and weekly seasonality (now the default)
Version 0.1 (2017.02.23)
- Initial release
Prophet is licensed under the [MIT license](LICENSE).
*Note that all licence references and agreements mentioned in the Prophet README section above are relevant to that project's source code only.