From 371e8a3bf4fea73e4c7752af9041d12716de2cf8 Mon Sep 17 00:00:00 2001 From: bl Date: Wed, 30 May 2018 17:02:47 -0700 Subject: [PATCH] Version bump --- R/DESCRIPTION | 13 +++++++------ R/man/generate_cutoffs.Rd | 8 ++++---- README.md | 4 +++- python/README | 2 +- python/fbprophet/__init__.py | 2 +- python/setup.py | 4 ++-- 6 files changed, 18 insertions(+), 15 deletions(-) diff --git a/R/DESCRIPTION b/R/DESCRIPTION index 44c6b73..b7db582 100644 --- a/R/DESCRIPTION +++ b/R/DESCRIPTION @@ -1,16 +1,17 @@ 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) diff --git a/R/man/generate_cutoffs.Rd b/R/man/generate_cutoffs.Rd index de60fb1..602a094 100644 --- a/R/man/generate_cutoffs.Rd +++ b/R/man/generate_cutoffs.Rd @@ -7,16 +7,16 @@ 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 diff --git a/README.md b/README.md index c7bc300..d9bb08f 100644 --- a/README.md +++ b/README.md @@ -2,10 +2,12 @@ [![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 diff --git a/python/README b/python/README index 776a92a..a3462be 100644 --- a/python/README +++ b/python/README @@ -1,7 +1,7 @@ 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 `_ released by Facebook's `Core Data Science team `_. diff --git a/python/fbprophet/__init__.py b/python/fbprophet/__init__.py index a2a7a0a..69342c8 100644 --- a/python/fbprophet/__init__.py +++ b/python/fbprophet/__init__.py @@ -7,4 +7,4 @@ from fbprophet.forecaster import Prophet -__version__ = '0.2.1.dev' +__version__ = '0.3' diff --git a/python/setup.py b/python/setup.py index 66fc591..344cd99 100644 --- a/python/setup.py +++ b/python/setup.py @@ -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 , Ben Letham ', @@ -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. """ )