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R/DESCRIPTION
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Package: prophet
Title: Automatic Forecasting Procedure
Version: 0.2.1.9000
Date: 2017-11-08
Version: 0.3
Date: 2018-06-01
Authors@R: c(
person("Sean", "Taylor", email = "sjt@fb.com", role = c("cre", "aut")),
person("Ben", "Letham", email = "bletham@fb.com", role = "aut")
)
Description: Implements a procedure for forecasting time series data 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.
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.
Depends:
R (>= 3.2.3),
Rcpp (>= 0.12.0)

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R/man/generate_cutoffs.Rd
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generate_cutoffs(df, horizon, initial, period)
}
\arguments{
\item{df}{Dataframe with historical data}
\item{df}{Dataframe with historical data.}
\item{horizon}{timediff forecast horizon}
\item{horizon}{timediff forecast horizon.}
\item{initial}{timediff initial window}
\item{initial}{timediff initial window.}
\item{period}{timediff Simulated forecasts are done with this period.}
}
\value{
Array of datetimes
Array of datetimes.
}
\description{
Generate cutoff dates

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README.md
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[![Build Status](https://travis-ci.org/facebook/prophet.svg?branch=master)](https://travis-ci.org/facebook/prophet)
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 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/).
The method is described in the paper: Sean J. Taylor, Benjamin Letham (2018) Forecasting at scale. The American Statistician 72(1):37-45.
## Important links

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python/README
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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 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/>`_.

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python/fbprophet/__init__.py
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from fbprophet.forecaster import Prophet
__version__ = '0.2.1.dev'
__version__ = '0.3'

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python/setup.py
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@ -98,7 +98,7 @@ with open('requirements.txt', 'r') as f:
setup(
name='fbprophet',
version='0.2.1',
version='0.3',
description='Automatic Forecasting Procedure',
url='https://facebook.github.io/prophet/',
author='Sean J. Taylor <sjt@fb.com>, Ben Letham <bletham@fb.com>',
@ -119,6 +119,6 @@ setup(
},
test_suite='fbprophet.tests',
long_description="""
Implements a procedure for forecasting time series data 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.
Implements 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.
"""
)