Automatic Forecasting Procedure

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.


Reference manual

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1.0 by Sean Taylor, 8 months ago

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Authors: Sean Taylor [cre, aut] , Ben Letham [aut]

Documentation:   PDF Manual  

Task views: Missing Data, Time Series Analysis

MIT + file LICENSE license

Imports dplyr, dygraphs, extraDistr, ggplot2, grid, lubridate, methods, RcppParallel, rstan, rstantools, scales, StanHeaders, stats, tidyr, xts

Depends on Rcpp, rlang

Suggests knitr, testthat, readr, rmarkdown

Linking to BH, Rcpp, RcppParallel, RcppEigen, rstan, StanHeaders

System requirements: GNU make, C++11

Imported by autoTS, bayesforecast, fable.prophet, modeltime, promotionImpact.

See at CRAN