Skip to main content
Join the official 2019 Python Developers SurveyStart the survey!

Automatic Forecasting Procedure

Project description

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.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for fbprophet, version 0.5
Filename, size File type Python version Upload date Hashes
Filename, size fbprophet-0.5.tar.gz (49.1 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page