In chris-prener/falseProphet: Automatic Forecasting Procedure

knitr::opts_chunk$set(collapse = T, comment = "#>")
options(tibble.print_min = 4L, tibble.print_max = 4L)
library(prophet)
library(dplyr)

This document provides a very brief introduction to the Prophet API. For a detailed guide on using Prophet, please visit the main site at https://facebook.github.io/prophet/.

Prophet uses the normal model fitting API. We provide a prophet function that performs fitting and returns a model object. You can then call predict and plot on this model object.

First we read in the data and create the outcome variable.

library(readr)
df <- read_csv('../tests/testthat/data.csv')

We call the prophet function to fit the model. The first argument is the historical dataframe. Additional arguments control how Prophet fits the data.

m <- prophet(df)

We need to construct a dataframe for prediction. The make_future_dataframe function takes the model object and a number of periods to forecast:

future <- make_future_dataframe(m, periods = 365)
head(future)

As with most modeling procedures in R, we use the generic predict function to get our forecast:

forecast <- predict(m, future)
head(forecast)

You can use the generic plot function to plot the forecast, but you must also pass the model in to be plotted:

plot(m, forecast)

You can plot the components of the forecast using the prophet_plot_components function:

prophet_plot_components(m, forecast)

chris-prener/falseProphet documentation built on April 15, 2022, 12:04 a.m.