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Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

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Quick Overview

Facebook Prophet is an open-source library for time series forecasting. It is designed to be easy to use, fast, and capable of handling data with strong seasonal effects and multiple seasonalities. Prophet is particularly well-suited for business forecasting tasks and can automatically detect trends, seasonality, and holidays in time series data.

Pros

  • Easy to use with sensible defaults, requiring minimal manual tuning
  • Handles missing data and outliers effectively
  • Incorporates holiday effects and custom seasonality
  • Provides uncertainty intervals for forecasts

Cons

  • May not perform as well as more complex models for certain types of data
  • Limited flexibility in model structure compared to some other forecasting methods
  • Can be computationally intensive for large datasets
  • Requires separate installation of Stan (a statistical modeling platform)

Code Examples

  1. Basic forecasting:
from prophet import Prophet
import pandas as pd

# Prepare the data
df = pd.DataFrame({
    'ds': pd.date_range(start='2022-01-01', periods=365),
    'y': np.random.randn(365).cumsum()
})

# Create and fit the model
model = Prophet()
model.fit(df)

# Make future predictions
future = model.make_future_dataframe(periods=30)
forecast = model.predict(future)

# Plot the forecast
fig = model.plot(forecast)
  1. Adding holidays:
holidays = pd.DataFrame({
  'holiday': 'thanksgiving',
  'ds': pd.to_datetime(['2022-11-24', '2023-11-23']),
  'lower_window': 0,
  'upper_window': 1,
})

model = Prophet(holidays=holidays)
model.fit(df)
  1. Customizing seasonality:
model = Prophet(
    yearly_seasonality=20,
    weekly_seasonality=False,
    daily_seasonality=False
)
model.add_seasonality(name='monthly', period=30.5, fourier_order=5)
model.fit(df)

Getting Started

To get started with Prophet, first install it using pip:

pip install prophet

Then, you can use Prophet in your Python code:

from prophet import Prophet
import pandas as pd

# Prepare your data
df = pd.DataFrame({
    'ds': pd.date_range(start='2022-01-01', periods=365),
    'y': np.random.randn(365).cumsum()
})

# Create and fit the model
model = Prophet()
model.fit(df)

# Make future predictions
future = model.make_future_dataframe(periods=30)
forecast = model.predict(future)

# Plot the results
fig = model.plot(forecast)

This basic example demonstrates how to create a Prophet model, fit it to your data, make predictions, and visualize the results.

Competitor Comparisons

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greykite

1,814

A flexible, intuitive and fast forecasting library

Pros of Greykite

  • More flexible and customizable forecasting models
  • Better handling of complex seasonality patterns
  • Improved performance for high-frequency time series data

Cons of Greykite

  • Steeper learning curve due to more complex API
  • Less extensive documentation compared to Prophet
  • Smaller community and fewer third-party resources

Code Comparison

Prophet:

from fbprophet import Prophet
model = Prophet()
model.fit(df)
future = model.make_future_dataframe(periods=365)
forecast = model.predict(future)

Greykite:

from greykite.framework.templates.autogen.forecast_config import ForecastConfig
from greykite.framework.templates.forecaster import Forecaster
forecaster = Forecaster()
result = forecaster.run_forecast_config(
    df,
    config=ForecastConfig(
        model_template="SILVERKITE",
        forecast_horizon=365
    )
)

Both Prophet and Greykite are powerful time series forecasting libraries, each with its own strengths. Prophet offers simplicity and ease of use, making it accessible for beginners. Greykite provides more advanced features and flexibility, catering to complex forecasting scenarios. The choice between the two depends on the specific requirements of the forecasting task and the user's expertise level.

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orbit

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A Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood.

Pros of Orbit

  • More flexible modeling approach, allowing for custom model structures
  • Better handling of multiple seasonalities and complex time series patterns
  • Supports Bayesian inference, providing uncertainty estimates for forecasts

Cons of Orbit

  • Steeper learning curve due to more complex API and model configurations
  • Less extensive documentation and community support compared to Prophet
  • May require more computational resources for complex models

Code Comparison

Prophet:

from fbprophet import Prophet
model = Prophet()
model.fit(df)
future = model.make_future_dataframe(periods=365)
forecast = model.predict(future)

Orbit:

from orbit.models import DLT
model = DLT(response_col='y', date_col='ds', seasonality=[52])
model.fit(df)
prediction = model.predict(df)

Both libraries offer concise APIs for time series forecasting, but Orbit provides more flexibility in model specification. Prophet's simplicity makes it easier to use for beginners, while Orbit's customization options cater to advanced users with complex forecasting needs.

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Pros of sktime

  • Broader scope: Supports various time series tasks beyond forecasting, including classification and regression
  • Modular design: Allows easy integration of custom algorithms and pipelines
  • Scikit-learn compatible: Seamlessly integrates with scikit-learn's ecosystem

Cons of sktime

  • Less specialized for forecasting: May not have as many built-in forecasting models as Prophet
  • Steeper learning curve: Requires more understanding of time series concepts and API structure

Code Comparison

sktime:

from sktime.forecasting.arima import ARIMA
from sktime.datasets import load_airline

y = load_airline()
forecaster = ARIMA(order=(1, 1, 1), seasonal_order=(1, 1, 1, 12))
forecaster.fit(y)
y_pred = forecaster.predict(fh=[1, 2, 3])

Prophet:

from prophet import Prophet
import pandas as pd

df = pd.read_csv('example_data.csv')
m = Prophet()
m.fit(df)
future = m.make_future_dataframe(periods=365)
forecast = m.predict(future)

Both libraries offer powerful time series forecasting capabilities, but sktime provides a more comprehensive toolkit for various time series tasks, while Prophet focuses specifically on forecasting with a simpler API.

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neuralforecast

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Scalable and user friendly neural :brain: forecasting algorithms.

Pros of neuralforecast

  • Offers a wider range of neural network-based forecasting models
  • Provides built-in support for probabilistic forecasting
  • Designed for scalability and handling large datasets

Cons of neuralforecast

  • Steeper learning curve due to more complex models
  • May require more computational resources for training
  • Less extensive documentation compared to Prophet

Code Comparison

Prophet:

from fbprophet import Prophet

model = Prophet()
model.fit(df)
future = model.make_future_dataframe(periods=365)
forecast = model.predict(future)

neuralforecast:

from neuralforecast import NeuralForecast
from neuralforecast.models import NBEATS

model = NeuralForecast(models=[NBEATS(input_size=7, h=1)])
model.fit(df)
forecast = model.predict()

Both libraries offer straightforward APIs for fitting models and generating forecasts. Prophet's API is slightly simpler, while neuralforecast provides more flexibility in model selection and configuration.

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neural_prophet

3,843

NeuralProphet: A simple forecasting package

Pros of Neural Prophet

  • Incorporates deep learning techniques, potentially capturing more complex patterns
  • Offers native support for multiple seasonalities and additional regressors
  • Provides uncertainty estimates for forecasts

Cons of Neural Prophet

  • Generally slower training and prediction times compared to Prophet
  • May require more data to achieve optimal performance
  • Less extensive documentation and community support

Code Comparison

Prophet:

from fbprophet import Prophet

m = Prophet()
m.fit(df)
future = m.make_future_dataframe(periods=365)
forecast = m.predict(future)

Neural Prophet:

from neuralprophet import NeuralProphet

m = NeuralProphet()
m.fit(df)
future = m.make_future_dataframe(df, periods=365)
forecast = m.predict(future)

Both libraries offer similar high-level APIs, making it easy to switch between them. The main differences lie in the underlying algorithms and additional features provided by Neural Prophet, such as the ability to incorporate deep learning components and handle multiple seasonalities more naturally.

Neural Prophet builds upon the ideas of Prophet while leveraging PyTorch for neural network capabilities. This allows for potentially more flexible and powerful models, especially when dealing with complex time series data. However, Prophet remains a solid choice for many forecasting tasks, particularly when simplicity and interpretability are prioritized.

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README

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

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 = <your existing cmdstan>)

# 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 1.1.6 (2024.09.29)

Python

  • Bug fixes: include predictions for dates with missing y the history, zero division error in cross validation metrics.
  • Changed NDArray[np.float_] to NDArray[np.float64] to be compatible with numpy 2.0

R

  • Updated holidays data based on holidays version 0.57.

Version 1.1.5 (2023.10.10)

Python

  • Upgraded cmdstan version to 2.33.1, enabling Apple M2 support.
  • Added pre-built wheels for macOS arm64 architecture (M1, M2 chips)
  • Added argument scaling to the Prophet() instantiation. Allows minmax scaling on y instead of absmax scaling (dividing by the maximum value). scaling='absmax' by default, preserving the behaviour of previous versions.
  • Added argument holidays_mode to the Prophet() instantiation. Allows holidays regressors to have a different mode than seasonality regressors. holidays_mode takes the same value as seasonality_mode if not specified, preserving the behaviour of previous versions.
  • Added two methods to the Prophet object: preprocess() and calculate_initial_params(). These do not need to be called and will not change the model fitting process. Their purpose is to provide clarity on the pre-processing steps taken (y scaling, creating fourier series, regressor scaling, setting changepoints, etc.) before the data is passed to the stan model.
  • Added argument extra_output_columns to cross_validation(). The user can specify additional columns from predict() to include in the final output alongside ds and yhat, for example extra_output_columns=['trend'].
  • prophet's custom hdays module was deprecated last version and is now removed.

R

  • Updated holidays data based on holidays version 0.34.

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.